Trading platforms using market sentiment and dynamic risk assessment profiles

ABSTRACT

The disclosure is directed to a trading platform and, more particularly, to systems and processes for simplifying market based investments using market sentiment and dynamic risk assessment profiles. A method implemented in a computer infrastructure has computer executable code tangibly embodied on a computer readable storage medium having programming instructions operable to: determine dynamic risk assessment profiles of different users; obtain trading information from disparate electronic sources; generate at least one investment opportunity with a risk profile using the trading information and matching the investment opportunity with the dynamic risk assessment profiles of a selected user or of the different users; and provide at least one trading recipe which is configured to convert the at least one investment opportunity into a simplified executable trade for the selected user.

FIELD OF THE INVENTION

The disclosure is directed to a trading platform and, more particularly,to systems and processes for simplifying market based investments usingmarket sentiment and dynamic risk assessment profiles.

BACKGROUND

Most trading platforms assess a customer's risk by asking profilingquestions about trading and trading habits to determine if a tradingrisk level is suitable for a customer. For instance, upon signing up fora brokerage account, a customer may be asked a series of questions todetermine their risk tolerance and trading acumen, e.g., if they arefamiliar with options trading. Based on many different questions, as anadded service, the brokerage can provide a static risk assessment to thecustomer which, in turn, can be used as a basic guide for tradingstocks, bonds, commodities, etc.

The static risk assessment does not provide any specific tradingstrategies such as which particular stocks should be selected by thecustomer, etc.; instead, the traditional trading approach requiresextensive researching by the user of a particular company, market, ordesired trading strategies, i.e., complex trading strategies such asstock options and futures trading. This approach requires a large amountof time to investigate the health and outlook of various companies ormarkets as well as understanding complex trading strategies such asstock options and futures trading. Moreover, complex strategies such astrading with options, puts, calls, becomes so difficult that even withextensive research the average investor will not attempt to trade withsuch market vehicles.

In other trading scenarios, stocks are screened based on a user'sself-assessed measurement of risk, that may match the behavioralassessment of risk. However, a user may become more or less adverse torisk over time for a number of reasons, including understanding themechanics of trading or changes to their personal or financialsituation. The use of a static profile or risk assessment leads toseveral problems, e.g.:

1. The use of a static profile or risk assessment does not account for auser's actual risk level as their trading evolves;

2. The use of a static profile or risk assessment may not represent thetrue risk level for a person, since the trader may think they are moreor less adverse to riskier trades than they really are based on actualtrading patterns; and

3. There is no way to profile and screen investment opportunities on auser's actual risk level and present opportunities that match the user'sideal level of risk when that risk level is inaccurate or based on outof date factors.

SUMMARY

In a first aspect of the invention, there is a method implemented in acomputer infrastructure having computer executable code tangiblyembodied on a computer readable storage medium having programminginstructions operable to: determine dynamic risk assessment profiles ofdifferent users; obtain trading information from disparate electronicsources; generate at least one investment opportunity with a riskprofile using the trading information and matching the investmentopportunity with the dynamic risk assessment profiles of a selected useror of the different users; and provide at least one trading recipe whichis configured to convert the at least one investment opportunity into asimplified executable trade for the selected user.

In another aspect of the invention, there is a computer program productcomprising one or more computer readable storage media having programinstructions collectively stored on the one or more computer readablestorage media, the program instructions executable to: obtain tradingprospects and sentiment of the trading prospects from a plurality ofelectronic sources; analyze the trading prospects and sentiment of thetrading prospects to determine a risk associated with each of thetrading prospects; package selected trading prospects as investmentopportunities with different fixed or configurable trading recipes eachof which have a different risk and/or investment outlook; provide thedifferent fixed trading recipes to one or more users in a personalizedlist; and receive execution instructions for at least one of thedifferent fixed trading recipes and accepting a simplified user actionto send the execution instructions to a brokerage account which isintegrated with a platform that generated the fixed trading recipes.

In another aspect of the invention, there is system comprising: a useraccount configured to maintain a user repository including anintelligent risk profile, authentication data, preferences, accountsettings and history of the user; an ingestion engine configured toingest data at scale, in real-time, the ingested data being ingestedfrom various incoming streams of content feeds; a machine learningengine configured to rate opportunities based on integration of theingested data including at least one sentiment that is a contextualmining of text that identifies and extracts subjective information fromthe ingested data, and use the ingested data with the risk profile togenerate different trading recipes of different opportunities withdifferent risk profiles for the user; an execution engine configured toallow the user to integrate the different trading recipes with anexternal brokerage account; and an event execution engine configured totrack the user's interactions and provide a scalable solution forcreating opportunity recommendations for the user with different tradingrecipes by working in conjunction with the machine learning engine. Theingestion engine, the machine learning engine, the execution engine andthe event execution engine run on a processor of the system, incombination with a computer readable memory, one or more computerreadable storage media, and program instructions collectively stored onthe one or more computer readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present invention are described in the detaileddescription which follows, in reference to the noted plurality ofdrawings by way of non-limiting examples of exemplary embodiments of thepresent invention.

FIG. 1 depicts a computing system according to aspects of the presentdisclosure.

FIG. 2 depicts a cloud computing environment according to aspects of thepresent disclosure.

FIG. 3 depicts an architectural environment which can be implemented inthe computing system of FIG. 1 and the cloud computing environment ofFIG. 2, according to aspects of the present disclosure.

FIG. 4 shows a recipe template catalog and resulting recipes generatedusing the systems and processes according to aspects of the presentdisclosure.

FIG. 5 is a non-limiting illustrative recipe (suggested investmentopportunity with underlying recipe for execution) using the systems andprocesses according to aspects of the present disclosure.

FIG. 6 shows a subset of components shown in FIG. 3 and theirinteractions in accordance with aspects of the present disclosure.

FIG. 7 shows a subset of components shown in FIG. 3 and theirinteractions in accordance with aspects of the present disclosure.

FIG. 8 shows a swim lane diagram of an exemplary process in accordancewith aspects of the invention.

FIG. 9 shows a swim lane diagram of an exemplary process usingartificial intelligence assisted by a user in accordance with aspects ofthe invention.

FIG. 10 shows a swim lane diagram illustrating a recipe executed by abroker bot and specific broker in a single commodity transaction inaccordance with aspects of the present disclosure.

FIG. 11 shows a block diagram showing a sentiment analysis in accordancewith aspects of the present disclosure.

FIG. 12 shows neural net inputs (e.g., sentiment) in accordance withaspects of the present disclosure.

FIG. 13 shows inputs for triggering a neural network in accordance withaspects of the present disclosure.

FIG. 14 shows an exemplary training of a neural network in accordancewith aspects of the present disclosure.

DETAILED DESCRIPTION

The disclosure is directed to a trading platform and, more particularly,to systems and processes for simplifying market based investments andrelated front end modules and backend architectures. More specifically,the systems and processes simplify market based investments by usingmarket sentiment and dynamic risk assessment profiles, amongst othertechnical solutions, to generate different trading opportunities andunderlying recipes which can be easily executed by the user (trader).For example, in embodiments, the systems and processes make it possiblefor non-professionals to benefit from the advantages of a professionaltrading approach while eliminating research time and deep knowledgenecessary for implementing complex trading strategies by generatingopportunity recommendations and then executing upon a single or multiplepreconfigured trading recipes. In this way and advantageously, thesystems and processes provide investment opportunities that greatlyreduce the complexity of option based trading of stocks or othercommodities across brokerage houses or by leveraging a simple investmentbrokerage account to perform group trades/fractional trades based onpreconfigured and/or fixed trading recipes.

Companies are building frameworks for simple social media to let peopleshare some of their trades, but the solutions lack the ability to pulldisparate relationships, such as user's risk level, preferred sectors,current assets and varying sentiment values, performance of sectors orindividual trades, amongst other relationships, to recommend investmentopportunities for trades on a stock level, let alone a futures level,such as options, that are comparably more complex than a simple stocktrade, amongst other technical problems. These and other technicalproblems are solved by the present disclosure in that the platformdescribed herein provides a way in which trades or other types ofcomplex investment transactions can be shared and packaged as anopportunity to the user in a simple framework for future execution ofcomplex trading opportunities.

Accordingly, the platform described herein solves technical problemsusing a technical solution lying computing systems and platforms todetermine different relationships, combine these different relationshipinto different types of trading opportunities backed by an executionrecipe, and package such opportunities and recipes to allow simplifiedand automated execution of complex transactions within an integratedbrokerage account, taking into consideration very disparate informationfrom disparate sources. With this noted, the present disclosure is notmerely displaying information for execution of a trade, but usesinformation gathered and processed from many different sources and makesmany different relationships and assumptions with such data tocontinuously package and update different trading opportunities (withdifferent risk levels) using sentiments of users, creators, the market,itself, and a host of other factors such as trends, business data, etc.Indeed, the specification notes that the platform described herein usescomplex algorithms and neural networks to create different tradingpackages which can be updated, in real-time, based on various differentfactors and which functions and advantages cannot be done on existingprocessing platforms, and that very specific techniques not previouslyknown to those skilled in the art are now shown to be implemented hereinto provide the advantages and functions of the platform. And, currently,a successful trader cannot possibly anticipate the market in the mannerdescribed herein, in that many different disparate factors, someseemingly unrelated and constantly changing, are used herein to providedifferent execution packages or opportunities.

In embodiments, the systems and processes can be implemented within anytype of trading exchange, including stocks, commodities, derivatives,swaps, and any combination of other financial instruments. In furtherembodiments, the systems and processes comprise a series of subsystemsthat enable users to take advantage of investment opportunities (withinany trading exchange) through a social powered ecosystem such as, e.g.,consumer sentiment, professional recommendations, etc., to generate andexecuted trading recipes. The trading recipes can also use variousfactors, e.g., volumes of all the buys and sells of different tradingalternatives, e.g., puts and calls, to formulate different preconfiguredrecipes, all of which are displayed to the user for their selection andexecution of certain trades. For example, by implementing artificialintelligence and machine learning methodologies, the systems andprocesses take into account community shared insights to formulatedifferent trading recipes (e.g., preconfigured trading opportunities)that best meet a user's needs. In this way, the systems and processesdescribed herein, as a whole, provide a technological feature thatsolves a technical problem using a technical solution; that is, thesystems and processes offers a preconfigured and personalized list ofinvestment opportunities with associated trading recipes, that allows atrader to understand and trade on complex trades as a single action, forany number of different trading scenarios.

Moreover, by taking into account market sentiment and dynamic riskassessment profiles, amongst other technical solutions, to generatepreconfigured opportunities and trading recipes, the systems andprocesses greatly simplifies understanding what a user can buy beforethey attempt to execute the recipe. And, in embodiments, because atrading recipe can include an entire pattern of puts, calls, buys andsells, etc., as a single purchase, there is no danger of starting amarket play that the investor cannot fulfill due to the fact that someof the trading opportunities (sells or buys) not being available whenthe investor executes the play (trade). Also, as a single trading recipecan be used for a plurality of complex trades, it is less likely thatthe investor will make a typing error buying too many contracts of aspecific part of the pattern since the recipe is set up for the user toautomate the various parts in a single action, etc. Lastly, andimportantly, by providing preconfigured trading recipes for executing anopportunity, it provides the investor with the ability to recognize,understand and discriminate between different trading opportunitiesusing the same commodities based on their risk profiles, amongst otherfactors, which are constantly and dynamically changing after each tradeor for other reasons. The complexity of recommending transactionsleverages a plurality of variables that match the investor with theinvestment package. For example, FIGS. 13 and 14, which are described inmore detail below, show how an investment opportunity exists in themarket and how the systems and processes assists a contributing user touse recipe templates to create specific instances of recipes forexecuting each of the identified opportunities based on the stock'svolatility, market sentiment, the current financials and past trendsetc. with the investor's tolerance for risk, their past buying andselling profile and their available capital recommending easily executedtrades using Multidimensional computation of suggested opportunitiespaired with multidimensional computation of inferred investor preferenceto yield recommendations that maximize execution rate.

Overview of System and Processes (Trading Platform)

The functional components of the systems and processes (tradingplatform) provides a simplified stock market based investment platformwhich can be implemented on the computing infrastructure of FIG. 1 andover the cloud computing system as outlined in FIG. 2. The followingdescribe the components of the systems and processes.

Investor

An investor is a person using the trading platform to find opportunitiesand use associated recipes to execute trades on the platform. Theinvestor is also referred to as a trader.

User Account

The user account is a master repository for connecting the user (e.g.,trader) to various roles (e.g., creator to investor) and variouscomponents of the systems such as brokerage accounts, etc. The useraccount maintains a user repository including a profile, authenticationdata, preferences, account settings and history. The user account alsomaintains connection profile information to their brokerage accounts, avirtual broker and an execution or broker bot, as examples.

Broker Integration Component

A broker integration component allows users to integrate the systems andprocesses described herein (referred to hereinafter also as a “tradingplatform”) with their external stock trading account at a traditionalonline stock brokerage house. This broker integration component allowsthe trading platform to invoke APIs of the external online stockbroker's platform to execute stock trading actions on behalf of theuser. The API is a set of programming code that queries data, parsesresponses, and sends instructions between one software platform, i.e.,the systems and processes described herein which simplify stock marketbased investments, and another platform, i.e., brokerage account orother external components.

In the context of trading, a trader will often use an API to establish aconnection between a set of automated trading algorithms and thetrader's preferred trading broker platform for the purpose of obtainingreal-time pricing data and place trades. These actions can be options,commodity purchases, derivatives, swaps, shorts, call options, futures,exchange traded funds, etc. and/or, interestingly, any combination ofany of these different trading schemes by a single user or a communityof users as described herein. As to the latter feature, the systems andprocesses described herein can create its own mutual type fund (e.g.,trading recipe) of different investment opportunities and seek a singleuser or a community of users to purchase the “mutual type fund” ofdisparate instruments based on risk factors of the individual orcommunity. So, it should be understood by those of ordinary skill in theart that the systems and processes described herein simplify marketbased investments for many different exchanges including security,commodity, derivatives, swaps and other financial instruments asdescribed herein.

Opportunity Registry

The opportunity registry is dynamic collection of configuredopportunities (e.g., investment opportunities which are preconfiguredrecipes for execution) available to users of the trading platform. Theopportunity registry is continuously updated by adding new opportunitiesand removing expired opportunities. The opportunity registry candetermine, by analysis, different trading opportunities and provide suchopportunities to the trader in a simplified opportunity.

Opportunity and Recipe

An opportunity is a package of information describing a market basedinvestment opportunity where a significant change in value is expectedin a relatively short amount of time. The information contained in theopportunity includes details of why a significant change is expected,when it will occur, and what factors would cause the change to be anincrease or a decrease in value. The opportunity package also includesan opportunity execution plan as a trading recipe, which details a setof trading steps required to execute the investment. Typically, thesetrading steps will include the ability for the user to choose betweeninvesting in an increase or in a decrease in value at varying degrees.

When a user executes an opportunity, the system can record the type ofopportunity in the opportunity registry or other database, and use thisdata to target the users in the future based on the type of opportunitythe user purchased. This is tracked in the order history of each accountholder. The opportunity creator matches opportunity characteristics withusers that typically selected and profited from similar opportunities inthe past. This matching could exist on any number of characteristicssuch as a specific company, a specific sector, a specific chart pattern,a specific type of news event such as a rumored merger or earningsevent, or a large short seller interest in the stock.

Intelligent Opportunity Creator

An intelligent opportunity creator is artificial intelligence (AI)consisting of a group of intelligent agents that look at pastopportunities, past trends in the markets, and additional data, such asnews feeds, social sentiment, risk profiles, etc., to determine if thereis a possible trading opportunity. If there is a recognized pastpattern, such as a company that follows a cyclic pattern over multiplecycles or receives data that may drive stock price, it can use this datato create new opportunities with trading recipes or reload a pastopportunity if there are trends that lead the system to determine theopportunity exists again.

The intelligent opportunity creator also continuously rates and ranksopportunities based on integration to various sentiments and machinelearning systems where news feeds and use forum data from communitysites are continuously scanned to calculate a trending user sentiment onthe specific opportunity, the company, the sector or the overall stockmarket trend. The intelligent opportunity creator can also (i) targetpast investors by looking at their order history and (ii) look at theorder history of current investors and recommend new opportunities basedon trends of what people are buying or looking at within currentopportunities, amongst other functionality described herein.

Accordingly, the opportunity is generated by the intelligent opportunitycreator (AI), which gathers information that may affect the price of atrade, e.g., an option, commodity, asset, exchange, etc. The AI cangather this information from a plurality of different sources, i.e.,crawling through a host of different databases over, e.g., the internet,including brokerage houses, financial institutions, financial sources(magazines), market analysis, social media, etc., collate thisinformation and provide an analysis as to how this information mayaffect prices of stocks, money exchanges, commodities, futures, etc.,which is then provided to the user for execution. In this way, theintelligent opportunity creator (AI), for example, can provide analysisas to why a significant change is to be expected based on a host ofdifferent factors, i.e., past trends and/or current market conditions,external factors, i.e., conflict in a certain oil producing region whichwould indicate fluctuations in gas and/or oil production which mayaffect pipeline or transportation issues, oil prices, etc. or a hostother internal company issues, external company issues, etc. The AI canalso determine trends based on past performances, either with aparticular company or industry as a whole. The AI can also look at pasttrading patterns of certain individuals, mutual funds, spiders, largeshareholders of certain companies, venture capital funds, etc. All ofthis information can be gathered together, how disparate they may be,for analysis, to determine and identify current trading opportunities.

Opportunity Execution Bot

An opportunity execution bot acts on behalf of the user once the userelects to invest in an opportunity as presented via a trading recipe.The opportunity execution bot (also referred to as the execution bot orbroker bot) processes the trading steps included in the trading recipethat is part of the opportunity package, and executes them via thebroker integration component. This process executes trades on stock oroptions or other types of trades in the user's external online brokerageaccount using the funds available in that account. The opportunityexecution bot is also responsible for periodically retrieving the statusof the investment such as the gain or loss and responsible foreventually closing any trades or positions based on the recipe tradingsteps in the opportunity package.

Opportunity Creator

An opportunity creator is a user that can modify an opportunity andassociated trading recipe already created by the AI (or other neuralnetworks), e.g., intelligent opportunity creator, which is then loadedinto the opportunity registry for other users to review and execute viathe associated recipes. The opportunity creator can also approveopportunities created by the intelligent opportunity creator, e.g., AI.

Opportunity Rules

The systems and processes described herein, e.g., opportunity engine,can use a business rules grammar for defining trades written in readableEnglish, but created by either or both the AI and the user. By way of anexample using dollar cost averaging, amongst other examples:

Name: “DollarCostAverage”

rule description: “Dollar Cost Averaging Sell”

condition: “SO>26 and opportunity is not expired”

actions: “sell SO of user shares”

rule description: “Dollar Cost Averaging Buy”

“condition”: “SO<25 and SO>23 and shares not already purchased andopportunity is not expire”

Condition: “SO opportunity is expired”

actions: “sell SO of user shares”

Actions: “buy SO of user shares”

Name: “Bull Call Spread”

rules description: “Bull Call Buy”

Actions: “buy call SO at 3.30 of user contracts*suggested buy units”

rule description: “Bull Call Sell”

Actions: “sell call SO at 1.50 of user contracts*suggested sell units”

Arbiter and Bots (i.e., Part of the Opportunity Creator)

An arbiter and one or more bots work together as part of the intelligentopportunity creator (AI) to spot new opportunities. For example, theintelligent opportunity creator can have bots that scan opportunitiesand trending charts, such as a head and shoulders chart or charts thatare looking for support line in a falling stock and mark/create anopportunity to buy or sell based on specific chart patterns, e.g., seeKirk Du Plessis, “13 Stock Chart Patterns That You Can't Afford ToForget”(https://optionalpha.com/13-stock-chart-patterns-that-you-cant-afford-to-forget-10585.html).

In embodiments, the arbiter can be the central gateway to newopportunities, and the bots can list possible opportunities to thearbiter. The arbiter can maintain posting rules and, e.g., can excludecertain industries, stocks or currencies to keep them from being postedas opportunities. The arbiter framework allows new bots to be easilyadded to perform work looking for opportunities and submit theseopportunities to the arbiter. For example, trending bots can be added tolook at what users are viewing for opportunities, as well as what usersare buying as opportunities, to recommend a higher rating for a currentopportunity look for similar opportunities in the same sector passed oncurrent charting trends, news and other factors that can be filtered bythe bot.

As another example of a bot implemented with the intelligent opportunitycreator, is a bot that looks at past opportunities, and can scan cyclicopportunities and patterns of a particular stock or stocks. The bot canreview the opportunities based on date ranges or other factors. Forinstance, some stocks typically trend higher in specific months andflatten or trend down in others. The bot can spot an opportunity byreviewing the data from market sources, such as financial sites,brokerage sites, or other financial news organizations for history data.The creator can then send notifications to users that made purchase ofthe past opportunity.

Leaderboard

The leaderboard tracks a list of most successful users over variousperiods of time such as weekly, monthly, quarterly and yearly (or otherpredetermined time period) based on the percentage gains they haveachieved with their picks and executions of opportunities. Theleaderboard also tracks a list of most successful opportunities andrecipes based on the summation of gains of all users that executed thatopportunity. The leaderboard also tracks a list of most successfulopportunity creators based on the total gains of all the users thatexecuted on opportunities created by that opportunity creator. Theinformation from the leaderboard can be used by the AI to determine bestpractices, trends and different opportunities, and using suchinformation generate new trading recipes or updated existing tradingrecipes.

Approver

An approver is any user with rights to publish an opportunity (i.e.,finished recipe) to the public. An opportunity creator or the AI canalso be their approver or work with a separate approver.

Virtual Brokerage

A virtual brokerage component provides the ability for a user to executeopportunities using the associated recipes, using an account with noactual monetary value. This virtual brokerage account can be provideddirectly in the trading platform or can be provided via the brokerageintegration component when connecting to a 3^(rd) party online brokeragethat offers virtual trading.

Configurable Execution of Opportunities

The trading platform allows the user the ability to configure anexecution for a selected opportunity by adjusting the strike price orother variable. For example, a user may expect the ABC stock to jumpmore than 6%. The user can select the opportunity fixed execution tradefor 4%+ and modify the strike price from 102 to a higher number such as104 with the expectation the stock will rise to 106. In this case, thecall option for 104 would be at a much lower price of $0.30. Each $30invested would return $200 for a profit of $170 (567%).

Premium Opportunities

The trading platform allows opportunity creators or the intelligentopportunity creator to classify an opportunity in a premium tier thatrequires users to purchase or acquire a certain privileged tier on thetrading platform before viewing premium opportunities. The tradingplatform collects any paid premium for privileged access and allocates apercentage of the revenue to opportunity creators of premiumopportunities.

Shared Success Opportunities

The trading platform allows opportunity creators or the intelligentopportunity creator to classify an opportunity as a shared successopportunity where they specify a price for executing the opportunity.The price is specified in a percentage of gains generated by the userwhen executing the opportunity. For example, the price of a sharedsuccess opportunity would be set at 5%. If a user executes the sharedsuccess opportunity and realizes a profit of $100, then that user wouldowe the opportunity creator a fee for using his opportunity. Inembodiments, no fee is owed if the opportunity does not result in aprofit.

Trends

The opportunity creator or the intelligent opportunity creator canobtain past trends using different databases, e.g., trends database.These different trends may be different stocks, commodities,derivatives, trends of different individuals or institutions. Inembodiments, the creator reviews the past and current trends byanalyzing the trends, and the creator can then create an opportunityfrom such analyzed trends. The opportunity is provided to theopportunity register. The opportunity register retrieves userpermissions from the account (user's accounts), which can be confirmedby the creator. The opportunity register will authorize the access tothe account and the opportunity register will create the opportunity forthe creator.

Trending Opportunity

For each opportunity, the trading platform maintains a count of how manyusers have executed the opportunity and which scenario each userselected. The trading platform displays to all users as part of theopportunity registry a list of trending opportunity scenarios that aremost selected by users. For example, the trading opportunity can be anopportunity most selected by other users or most selected by specificpreferences set or inferred by the user. For instance, if the user likesbull trades or if the user likes a specific sector, these may filterhigher on the trending opportunity because they match preferences fromthe user's history.

The opportunity engine can identify possible dollar cost averagingpatterns, as an example. For example, these are stocks where the stockfloats between a small range or even remains almost level. For instance,a stock may constantly trade between $25 and $26 dollars. If there is agood fluctuation of the stock, between $25 and $26, the opportunityengine can setup a trade the buys at $25 and sells at $26 for a definedperiod of time. For instance, an opportunity could run for a month whereevery time the stock drops to $25, the opportunity engine willautomatically buy the stock. The opportunity engine can add stop on thestock will automatically sell the stock if the stock falls below $22 tolimit losses. During this time period, the stop will automatically buywhen it falls under $25 a share. When the stock reaches $26, theexecution engine will automatically sell the stock. When the stock dropsto $25 again, the systems and processes described herein, e.g.,execution engine, will purchase the stock again until the stock reaches$26 a share. If there is 100 units of the stock and this smallfluctuation happens 50 times during the month, it is possible to profitabout $5,000.

Watch List

If a user is interested in an opportunity, but does not want to investusing their real or virtual account, the user may elect to add theopportunity to a watch list. As part of this process, the user can makea selection on the opportunity scenario they think is most likely tooccur. The trading platform will maintain a list of all opportunities onthe user's watch list and indicate the status of the opportunity alongwith the expected gain/loss percent if the user had executed theopportunity. Additionally, the trading platform will include a count ofusers watching each opportunity as part of the opportunity registry.

Notifications

Notification and alerts provide information to the user (investor oropportunity creator). The user can configure various alerts andnotifications. For example, a user will receive notifications based onthe progress of their executed opportunities or opportunities on theirwatch list. Users can also receive notifications on trendingopportunities or new opportunities created by certain opportunitycreators. For example, if the opportunity creator recommends to closeout a trade, the user may receive a notification that the user needs toclose the order out or the platform can automatically close the orderout and inform the user. Closing an account can include, e.g., sellingoff the positions.

Community Funded Investment

Some opportunities require a significant investment to get started.These cases can be referred to as mega opportunities, and they mayrequire investment from multiple investors in the market. Users can buyinto an unfunded opportunity that will execute for a group of users byonce funded or will expire if the opportunity expires without debitingany funds from the users brokerage accts.

For instance, an investor may want to purchase a part of a specific realestate, but cannot afford to buy the entire property. The systems andprocesses are configured to allow a holding company to fund the purchaseof the real estate by multiple investors with a probable payout over aperiod of time. This is called an unfunded opportunity. Users could alsobuy into direct shares of large stocks or even option buying that wouldotherwise be unaffordable by buying into a percentage of an actual shareor option. In this case, an opportunity is created allowing a user tobuy a share of a certain stock or a certain portion of an option for aset amount of time and money.

Moreover, in other cases, the user could buy into a mix of opportunitiessuch where they spread their money across a group of opportunitiesfunded as a single opportunity. (This is a mutual fund of buys intomultiple options instead of a single option.) In this case, funding thelarger purchase may be out of the reach of the average investor andrequire the investment of multiple investors.

Computing Infrastructure and Environment Implementing the TradingPlatform

FIG. 1 depicts a computing system according to aspects of the presentdisclosure. In embodiments, the computing system 100 can berepresentative of a system, a method, and/or a computer program productat any possible technical detail level of integration. The computingsystem 100 is capable of providing and integrating all of thefunctionality described herein, including, for example, (i) creating andpresenting any number of different opportunities as trading recipes byanalyzing, collating and presented data in a usable format through thetrading recipes of different complex trading opportunities, (ii)creating and presenting any number of different notifications, (iii)integrating with external components, e.g., brokerage accounts, by usingAPIs, (iv) implementing the different components such as intelligentopportunity creator (AI), arbiter, etc., and (v) analyzing data toprovide trends, etc., and using the trends and other information whengenerating different trading recipes, as some examples.

The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor 115 to carry out aspects of the presentinvention as described herein. The computer readable storage medium canbe a tangible device that can retain and store instructions for use byan instruction execution device. The computer readable programinstructions may be stored in the computer readable storage medium thatcan direct the computer system 100, a programmable data processingapparatus, and/or other devices to function in a particular manner, suchthat the computer readable storage medium having instructions storedtherein comprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock or blocks of the architectural (block) diagram.

The computer readable storage medium may be, for example, but is notlimited to, an electronic storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, and any suitable combination of theforegoing. A computer readable storage medium or media, as used herein,is not to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

The computer readable program instructions described herein can bedownloaded to respective the computing/processing device (e.g.,computing system 100) from the computer readable storage medium or to anexternal computer or external storage device via a network, for example,the Internet, a local area network, a wide area network and/or awireless network. Computer readable program instructions for carryingout operations of the present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more known programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage or any combination thereof including partly or entirely on anycombination. A remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Referring now specifically to FIG. 1, an illustrative architecture of acomputing system 100 implemented as embodiments of the present inventionis shown. The computing system 100 is only one example of a suitablecomputing system and is not intended to suggest any limitation as to thescope of use or functionality of the systems described herein. Also,computing system 100 should not be interpreted as having any dependencyor requirement relating to any one or combination of componentsillustrated in computing system 100.

As shown in FIG. 1, the computing system 100 includes a computing device105. The computing device 105 can be resident on a networkinfrastructure such as within a cloud environment (as depicted in FIG.2), or may be a separate independent computing device (e.g., a computingdevice of a third party service provider). The computing device 105 mayinclude a bus 110, the processor 115, a storage device 120, a systemmemory (hardware device) 125, one or more input devices 130, one or moreoutput devices 135, and a communication interface 140.

The bus 110 permits communication among the components of computingdevice 105. For example, bus 110 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures toprovide one or more wired or wireless communication links or paths fortransferring data and/or power to, from, or between various othercomponents of computing device 105.

The processor 115 may be one or more conventional processors ormicroprocessors that include any processing circuitry operative tointerpret and execute computer readable program instructions, such asprogram instructions for controlling the operation and performance ofone or more of the various other components of computing device 105. Inembodiments, the processor 115 interprets and executes the processes,steps, functions, and/or operations of the present invention, which maybe operatively implemented by the computer readable programinstructions. For example, the processor 115 can execute any of thefunctionality of the functional components, swim lane diagrams, blocksof the block diagrams or any portions or combinations thereof asdescribed herein. For example, the processor can be representative anyof the following components of FIG. 3, including (i) ingestion layer,(ii) analytics, (iii) learning, (iv) engagement, (v) user account, (vi)micro-services, (vii) query layer, (viii) event layer or any of itssubcomponents. It should be recognized by those of ordinary skill in theart, that each layer of component can be a separate “engine” or acombination of engines that execute upon the functionality providedherein and particularly those shown in the architecture of FIG. 3.

In embodiments, processor 115 may receive input signals from one or moreinput devices 130 and/or drive output signals through one or more outputdevices 135. The input devices 130 may be, for example, a keyboard ortouch sensitive user interface (UI) as further described below. Theoutput devices 135 can be, for example, any display device, printer,etc., as further described below.

The storage device 120 may include removable/non-removable,volatile/non-volatile computer readable media, such as, but not limitedto, non-transitory media such as magnetic and/or optical recording mediaand their corresponding drives. The storage device 120 can berepresentative of the opportunity registry (recipe repository), amongstother storage systems. Also, the drives and their associated computerreadable media provide for storage of computer readable programinstructions, data structures, program modules and other data foroperation of computing device 105 in accordance with the differentaspects of the present invention. For example, in embodiments, thestorage device 120 may store operating system 145, application programs150, and program data 155 in accordance with aspects of the presentinvention. In embodiments, the storage device 120 can be an opportunityregister.

The system memory 125 may include one or more storage mediums, includingfor example, non-transitory media as already described herein. Inembodiments, an input/output system 160 (BIOS) including the basicroutines that help to transfer information between the various othercomponents of the computing device 105, such as during start-up, may bestored in the ROM. Additionally, data and/or program modules 165, suchas at least a portion of operating system 145, application programs 150,and/or program data 155, that are accessible to and/or presently beingoperated on by processor 115 may be contained in the RAM.

The one or more input devices 130 may include one or more mechanismsthat permit an operator to input information to computing device 105,such as, but not limited to, a touch pad, dial, click wheel, scrollwheel, touch screen, one or more buttons (e.g., a keyboard), mouse,controller, track ball, microphone, camera, proximity sensor, lightdetector, motion sensors, biometric sensor, and combinations thereof.The one or more output devices 135 may include one or more mechanismsthat output information to an operator, such as, but not limited to,audio speakers, headphones, audio line-outs, visual displays, antennas,infrared ports, tactile feedback, printers, or combinations thereof.

The communication interface 140 may include any, e.g., networkinterface, network adapter, modem, or combinations thereof, etc., thatenables the computing device 105 to communicate with remote devices orsystems, such as a mobile device or other computing devices such as, forexample, a server in a networked environment, e.g., cloud environment.For example, computing device 105 may be connected to remote devices orsystems via one or more local area networks (LAN) and/or one or morewide area networks (WAN) using communication interface 140.

As discussed herein, the computing system 100 may be configured toprovide any combination of the functions described herein, includingthose of which are described in the function components of any of theswim lane diagrams and/or any other block diagram or flow chartsprovided herein. In particular, computing device 105 may perform tasks(e.g., process, steps, methods and/or functionality) in response to theprocessor 115 executing program instructions contained in a computerreadable medium, such as the system memory 125. The program instructionsmay be read into the system memory 125 from another computer readablemedium, such as the data storage device 120, or from another device viathe communication interface 140 or server within or outside of a cloudenvironment.

In embodiments, an operator may interact with the computing device 105via the one or more input devices 130 and/or the one or more outputdevices 135 to facilitate performance of the tasks and/or realize theend results of such tasks in accordance with aspects of the presentinvention. In additional or alternative embodiments, hardwired circuitrymay be used in place of or in combination with the program instructionsto implement the tasks, e.g., steps, methods and/or functionality,consistent with the different aspects of the present invention. Thus,the steps, methods and/or functionality disclosed herein can beimplemented in any combination of hardware circuitry and software.

FIG. 2 shows an exemplary cloud computing environment 200. The cloudcomputing is a computing model that enables convenient, on-demandnetwork access to a shared pool of configurable computing resources,e.g., networks, servers, processing, storage, applications, andservices, that can be provisioned and released rapidly, dynamically, andwith minimal management efforts and/or interaction with the serviceprovider. In embodiments, one or more aspects, functions and/orprocesses described herein may be performed and/or provided via cloudcomputing environment 200.

As depicted in FIG. 2, the cloud computing environment 200 includescloud resources 205 that are made available to client devices 210 via anetwork 215, such as the Internet. The cloud resources 205 can include avariety of hardware and/or software computing resources, such asservers, databases, storage, networks, applications, and platforms. Thecloud resources 205 may be on a single network or a distributed network.The cloud resources 205 may be distributed across multiple cloudcomputing systems and/or individual network enabled computing devices.The client devices 210 may comprise any suitable type of network-enabledcomputing device, such as servers, desktop computers, laptop computers,handheld computers (e.g., smartphones, tablet computers), set top boxes,and network-enabled hard drives. The cloud resources 205 are typicallyprovided and maintained by a service provider so that a client does notneed to maintain resources on a local client device 210. In embodiments,the cloud resources 205 may include one or more computing system 100 ofFIG. 1 that is specifically adapted to perform one or more of thefunctions and/or processes described herein.

The cloud computing environment 200 may be configured such that thecloud resources 205 provide computing resources to client devices 210through a variety of service models, such as Software as a Service(SaaS), Platforms as a service (PaaS), Infrastructure as a Service(IaaS), and/or any other cloud service models. The cloud resources 205may be configured, in some cases, to provide multiple service models toa client device 210. For example, the cloud resources 205 can provideboth SaaS and IaaS to a client device 210. The cloud resources 205 maybe configured, in some cases, to provide different service models todifferent client devices 210. For example, the cloud resources 205 canprovide SaaS to a first client device 210 and PaaS to a second clientdevice 210. The client devices can be, e.g., a brokerage platform and/orthe architecture shown in FIG. 3, amongst other clients.

The cloud resources 205 may be configured to provide a variety offunctionality that involves user interaction. Accordingly, a userinterface (UI) can be provided for communicating with cloud resources205 and/or performing tasks associated with cloud resources 205. The UIcan be accessed via a client device 210 in communication with cloudresources 205. The UI can be configured to operate in a variety ofclient modes, including a fat client mode, a thin client mode, or ahybrid client mode, depending on the storage and processing capabilitiesof cloud resources 205 and/or client device 210. Therefore, a UI can beimplemented as a standalone application operating at the client devicein some embodiments. In other embodiments, a web browser-based portalcan be used to provide the UI. Any other configuration to access cloudresources 205 can also be used in various implementations.

The cloud computing environment 200 may be configured such that cloudresources 205 provide computing resources to client devices 210 througha variety of deployment models, such as public, private, community,hybrid, and/or any other cloud deployment model. Cloud resources 205 maybe configured, in some cases, to support multiple deployment models. Forexample, cloud resources 205 can provide one set of computing resourcesthrough a public deployment model and another set of computing resourcesthrough a private deployment model.

One or more cloud resources 205 may be conceptually structured inmultiple layers. In one example, the layers include a firmware andhardware layer, a kernel layer, an infrastructure service layer, aplatform service layer, and an application service layer. The firmwareand hardware layer may be the lowest layer upon which the other layersare built, and may include generic contributing nodes (e.g., datacenters, computers, and storage devices) geographically distributedacross the Internet and provide the physical resources for implementingthe upper layers of the cloud service provider. The kernel layer isabove the firmware and hardware layer and may include an operatingsystem and/or virtual machine manager that host the cloud infrastructureservices. The kernel layer controls and communicates with the underlyingfirmware and hardware layer through one or more hardware/firmware-levelapplication programming interfaces (APIs). The infrastructure servicelayer is above the kernel layer and may include virtualized resources,such as virtual machines, virtual storage (e.g., virtual disks), virtualnetwork appliances (e.g., firewalls), and so on. The infrastructureservice layer may also include virtualized services, such as databaseservices, networking services, file system services, web hostingservices, load balancing services, message queue services, map services,e-mail services, and so on. The platform service layer is above theinfrastructure service layer and may include platforms and applicationframeworks that provide platform services, such as an environment forrunning virtual machines or a framework for developing and launching aparticular type of software application. The application service layeris above the platform service layer and may include a softwareapplication installed on one or more virtual machines or deployed in anapplication framework in the platform service layer. The softwareapplication can also communicate with one or more infrastructure servicecomponents (e.g., firewalls, databases, web servers, etc.) in theinfrastructure service layer.

In another example, one or more cloud resources 205 may be conceptuallystructured in functional abstraction layers including a hardware andsoftware layer, a virtualization layer, a management layer, and aworkloads layer. The hardware and software layer may include hardwareand software components such as mainframes, RISC (reduced instructionset computer) architecture based servers, storage devices, networks andnetworking components, application server software, and databasesoftware. The virtualization layer may include virtual entities such asvirtual servers, virtual storage, virtual networks, virtualapplications, and virtual clients. The management layer may providefunctions such as resource provisioning, metering and pricing, security,user portals, service level management, and service level agreementplanning and fulfillment. The workloads layer may provide functions forwhich the cloud computing environment is utilized, such as mapping andnavigation, software development and lifecycle management, dataanalytics and processing, and transaction processing.

In embodiments, software and/or hardware that performs one or more ofthe aspects, functions and/or processes described herein may be accessedand/or utilized by a client (e.g., an enterprise or an end user) as oneor more of an SaaS, PaaS and IaaS model in one or more of a private,community, public, and hybrid cloud. Moreover, although this disclosureincludes a description of cloud computing, the systems and methodsdescribed herein are not limited to cloud computing and instead can beimplemented on any suitable computing environment.

Moreover, it is understood that although this disclosure includes adescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed. For example, the platform can implement resourcepooling in which the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. Moreover, the platform has rapid elasticity capabilities whichenable the platform to be rapidly and elastically provisioned, in somecases automatically, to scale out and rapidly released to quickly scalein. The platform may also be implemented in different service modelsincluding, Software as a Service (SaaS), Platform as a Service (PaaS)and Infrastructure as a Service (IaaS). The cloud implementation can bedeployed on a private cloud, community cloud, public cloud or a hybridcloud as is known in the art.

Architectural Environment

The block diagrams in the Figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods, and computer program products according to various embodiments.In this regard, each block in the block diagrams may represent a module,segment, or portion of instructions, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). In implementations, the functions noted in the blocks mayoccur out of the order, depending upon the functionality involved. Itwill also be noted that each block of the block diagrams, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions suchas shown in FIG. 1.

More specifically, FIG. 3 depicts an architectural environment which canbe implemented in the computing system 100 of FIG. 1 and/or the cloudcomputing environment of FIG. 2. Generally speaking and as described ingreater detail herein, by using the architectural environment of FIG. 3,an investor can interact with the systems and processes describedherein, which uses a series of micro-services that easily integrateacross mobile, web, chat or other emerging channels. The architecturalenvironment shown in FIG. 3 will generate preconfigured trading recipesconsisting of prepackaged opportunities based on investor profile,investor or other sentiment, and other data analytics described herein.The architectural environment will allow the investor browse the tradingrecipes, while also tracking the user's current interactions in order tofurther refine or to tailor trading recipes to a current session basedon factors described herein. The architectural environment will alsoallow the user to execute real and virtual trading opportunities basedon a number of different scenarios provided in the prepackaged tradingrecipes.

In embodiments, the architectural environment 300 includes the followingcomponents. And, as already noted herein, each of the followingcomponents can be a separate “engine” or a combination of engines thatexecute upon the functionality provided herein and particularly thoseshown in the architecture of FIG. 3.

Ingestion Layer 305

The ingestion layer 305 is an ingestion engine which allows the entiresolution (trading platform) to effectively ingest large amounts of data310 at scale. For example, the ingestion layer 305 is used to intake andmove data into storage, e.g., storage system 120, at scale. This data isobtained from content feeds 310 such as, for example, news, orderhistory (of one or more users), market data, recipe history (as furtherdescribed herein), social data (social feeds, e.g., Twitter®, Facebook®,MotleyFool®, etc.,) and processional data, amongst other data types, inaddition to or optionally any data described in the section entitled“OVERVIEW OF SYSTEM AND PROCESSES (TRADING PLATFORM)”. Additional datamay include sentiment, as described herein.

In a specific embodiment, the ingestion layer 305 implements open sourceprocessing systems such as Apache Kafka to build real-time data pipelines and streaming applications in accordance with aspects of thepresent disclosure. (Kafka is a registered trademark of The ApacheSoftware Foundation at least in the United States.) Kafka is anopen-source stream-processing software platform written in Scala andJava; although other languages are also contemplated herein as alreadydescribed above. In embodiments, the processes and systems describedherein, including Kafka, are horizontally scalable and fault-tolerant.Moreover, the platform aims to provide a unified, high-throughput,low-latency platform for handling real-time data feeds.

In implementation, the ingestion layer 305 using Kafka, for example,ingests streams of data received from the content feeds 310, e.g.,market data, social data, etc., obtained from the web or other sourcesat scale. The content feed 310 can also include user provided data, inwhich the user can execute as contributors by providing additionalcomments or ratings on an opportunity or creating variations ofunderlying recipes that act on the same underlying stock or othertrades, but uses different parameters to modify the risk/reward of theoriginal trading recipe or could also modify the trading recipe toincrease in value of the underlying stock moves in the oppositedirection assumed in the original recipe. In these latter scenarios, theuser can be an opportunity creator, which assists the intelligentopportunity creator (e.g., artificial intelligence).

In embodiments, the artificial intelligence, e.g., Kafka, can also serveas an event engine where learning systems, such as Apache SystemML® andPredictionIO® can subscribe to incoming streams (e.g., content feeds310) to trigger recipe events based on learning rules, as should beunderstood by those of ordinary skill in the art such that no furtherexplanation. For example, Apache SystemML provides a workplace formachine learning using big data, where it automatically scales data.PredictionIO is used to create predictive engines for any machinelearning task such as trends, notifications and opportunities in orderto create and/or generate the trading recipes comprising a host ofdifferent complex trading opportunities based on the external andinternal factors described herein.

As specifically noted at https://predictionio.apache.org/, PredictionIOallows a developer/program platform to:

-   -   quickly build and deploy an engine as a web service on        production with customizable templates;    -   respond to dynamic queries in real-time once deployed as a web        service;    -   evaluate and tune multiple engine variants systematically;    -   unify data from multiple platforms in batch or in real-time for        comprehensive predictive analytics;    -   speed up machine learning modeling with systematic processes and        pre-built evaluation measures;    -   support machine learning and data processing libraries such as        Spark MLLib and OpenNLP;    -   implement your own machine learning models and seamlessly        incorporate them into your engine;    -   simplify data infrastructure management.

The streamed data can be moved to scalable/persistent long term storageon, for example, a simple Storage Service (e.g., S3) or other storagedevice as shown in FIG. 1, e.g., storage device 120. The storage devicealso can store other information such as the trading recipes (i.e.,opportunities which are packaged in a certain manner by the AI ormodified by an opportunity creator).

This movement of data can be provided by Secor. The Secor also allowsthe data to be further segmented at scale by, e.g., Apache Spark andHadoop. It should be understood by those of skill in the art that datalost or corrupted at this stage is not recoverable so the greatestdesign objective for Secor is data integrity.

Scheduler allows for the pull ingestion of data when needed. Some datadoes not fire in events, but must be downloaded or requested from aprovider. The scheduler provides the ability to pull data from, e.g.,the content stream 310, web services, FTP, RSS or other feeds whenneeded.

Analytics 315

The analytics 315 serves multiple functions within the solution (i.e.,trading platform) including providing statistical feedback on the dataallowing the trading platform to provide quick access to graphs andtrends on a user's current account as well as market data on stocks orother trading opportunities. For example, the analytics 315 can tracktrading recipe views, trading recipe success and trading recipe losses,at scale.

In embodiments, in the analytics 315, by way of one example, cloud datawarehouse (i.e., RedShift) also allows the machine learning systems toview a past history of any particular trading recipe to understand whyit was successful or unsuccessful. For instance, if a company was on adownward trend, but was suddenly gaining attention in the news and onsocial media for increased sales, increased visibility etc., thelearning system can view past trading recipes that were successful andcreate new trading recipes based on viewing current streams (from Kafka)and reviewing past analytics from similar or other positions.

Storage 120

The storage 120 ingests vast amounts of data at scale. For example, thestorage 120 is used to store the trading recipes (prepackaged tradingopportunities generated by the systems and processes) that an investorcan browse and execute. The storage 120 includes a recipe and/oropportunity repository which serves as the holding point for currenttrading recipes created by contributors, opportunity creators and/or themachine learning systems (AI). Also, the data from the content feed 310can be easily ingested and stored in the storage using, e.g., ApacheSpark and Hadoop, for indexing.

Machine Learning 320

The machine learning 320 is the intelligent opportunity creator thatcontinuously rates, ranks opportunities based on integration of varioussentiments and other relevant data (using machine learning) from thedata feeds 310, and uses this information to generate different tradingrecipes of the opportunities. The machine learning 320 provides machinelearning at scale as vast amount of data will come in quickly. In thisway, the machine learning 320 provides a solution to create tradingrecipes that are relevant to the investor as it contains scalableaspects for processing and interpreting data. For example, the machinelearning 320 can access the analytics 315 and storage 120 to generateand propose new trading recipes based on past history of a commodity, aswell as current trends, sentiments, etc., while applying proveninvestment patterns as trading recipes that are stored in the storage120. The machine learning 320 can also create the trading recipes byanalyzing many other factors including, (i) at order history of currentinvestors and recommend options based on trends, and analysis andintegration of the different data from the content feeds 310 amongstother functionality described herein.

The machine learning 320 can also use sentiment analysis, which is acontextual mining of text that identifies and extracts subjectiveinformation in source material, and which helps to understand the socialsentiment of a brand, product or service while monitoring onlineconversations. This can be accomplished using Self Organizing FuzzyNeural Networks (SOFNN) as is known in the art. See, also, e.g.,Shashank Gupta, Sentiment Analysis: Concept, Analysis and Applications,Jan. 7, 2018(https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17)and Khan Saad Bin Hasan, Stock Prediction Using Twitter Ever wondered ifyou could predict the stock market what you can do?, Jan. 3, 2019(https://towardsdatascience.com/stock-prediction-using-twittere432b35e14bd?gi=cc7bead1311e.)

In embodiments, the sentiment analysis is carried out by the machinelearning 320 performed, e.g., for the overall market, a specificstock/commodity and social sentiment about the stock. The machinelearning 320 can also be used to produce positive, negative and neutralsentiments for each stock/commodity or sector, in combination with morefactually based market data such as price trends and other objectiveevidence of market concern, e.g., sales, Earnings before interest, tax,depreciation and amortization (EBITDA), etc.

In embodiments, the sentiment can be computed by looking for words thatare written about the particular trading position, e.g., management orhow people on social media speak positively or negatively about thecompany. The sentiment can even be presented to the user, prior toincorporating into the analysis, to determine whether the particularuser is in agreement or not in agreement with the sentiment. During theingestion of data from the content feed 310, e.g., news, social data,professional data and market data, different sentiments for each factorcan be established and used to understanding how people, the market andprofessionals think about a stock/sector etc.

In operation, the machine learning 320 can implement Apache Spark toprocess and split data into smaller sets, add new partitions for storageand process a first level of analytics on incoming data, as well asprovide a base level of machine learning algorithms such as alternatingleast squares for recommendations. The analytics includes looking fortrends, analyzing prices and other complex trades, and collating of datareceived in the content feed 310, to develop trading recipes consistingof prepackaged trading opportunities.

In addition, Apache SystemML can be used to provide the workplace formachine learning using the big data. The SystemML can be run on top ofApache Spark, where it automatically scales data, line by line,determining whether code should be run on the driver or an Apache Sparkcluster, as an example. In the present disclosure, SystemML can providelearning algorithms that run across multiple datasets. SystemML can alsocompare past history of recipes and their success or failure along witha user's profile information, including investment types, sectors, risketc. to determine if a user should be recommended a specific recipe. Itis understood, though, that other machine learning algorithms can beused to provide the functionality herein, including neural networks,etc.

In further embodiments, the machine learning 320 can use System MLbeyond the act of a typical recommendation engine by creating newtrading recipes based of past analytics stored on trading recipes thatwere successful or failed, as well as the risk level initially assignedto the user or as dynamically determined by the systems and processesbased on the investors past transactions, past and current research,etc. Moreover, the systems and processes herein using, e.g., System MLand PredictionIO, can look at the data feeds 310, e.g., social media,market trends and news data, to determine similarities with past spikesand create new trading recipes based on a set of common patterns tooffer new trading recipes to a community. The machine learning 320 canalso implement Apache Ignite to provide a constant learning system formachine learning using scalable, in memory solutions for solving complexdata problems, including money management and other complex tradingopportunities as described herein.

In further embodiments, an arbiter can be used with the rules engine inrating as well as holding interrelationships between sectors andcommodities. In embodiments, the arbiter can be a central gateway to newopportunities and provide analysis as to whether certain informationshould be considered for generation of a trading recipe. In addition,the arbiter can maintain posting rules, for instance, the arbiter mayexclude certain industries, stocks or currencies to keep them from beingposted as opportunities.

For example, in embodiments, the arbiter can take results from multiplealgorithms and make an overall decision based on all the algorithms thatare run on the platform. In this way, the arbiter can be an overalldecision maker. And, the arbiter is an artificial intelligence pattern,but it is important to note that the overall platform has a mechanism(e.g., arbiter) for looking at and over all artificial intelligenceprocesses and rules running on the platform and, taking all of them intoaccount, make a larger decision as to a possible trade, etc. In thisway, the arbiter can also post opportunities as premium based on rulesthat can be easily changed in the arbiter using the rules engine. Thearbiter can also use the combination of sentiments to determine if itneeds to look for upside/positive plays, negative plays or plays thatkeep the stock running in a neutral area, such as a spread play. Forinstance, the social sentiment, market sentiment, sentiment about themanagement and professional sentiment on a stock are positive, thearbiter will hypothesize that the stock is going up and look for recipesthat promote the stock/commodity moving up.

The arbiter can also be used to search and filter trading recipes basedon a user's past purchase history, risk level, sectors of interest,favorite contributors, bull runs, bear runs, spread plays, short sells,and other factors. In this case, an arbiter can take into accountmultiple factors from the user account, including order history and riskprofile to look at what recommendations to make to the trader based ongenetic algorithms and recommendation algorithms like alternating leastsquares (ALS). In addition, just as with creation patterns, the arbitercan be used to search for recommendations over multiple algorithms, findsimilar recipes or recipes that fit a user's profile and return themback to the user as possible recipes to execute, etc.

In further embodiments, the machine learning 120 using, e.g., the rulesengines, can provide a risk profile. For example, the machine learning120 can assigned a trading recipe a risk rating of very low, low,medium, high, extremely high, speculative, to each opportunity or theset of opportunities in the trading recipe. Similarly, the machinelearning 120 can assess the trading habits of the investor and assign arisk profile rating to the investor. In this way, each trade and eachinvestor can be assigned a numeric value associated with risk. When auser executes a trade, a risk profile engine can adjust the user's riskprofile by the numeric value of the trade using the following formula.The machine learning 120 can then use the risk ratings to match thepreconfigured recipe with the risk tolerance of the investor.

Execution Layer 325

The execution layer 325 includes a broker integration component whichallows users to integrate the trading platform described herein withtheir external brokerage account at a traditional online stock broker.This allows the trading platform described herein to invoke the APIs ofthe external online stock broker's platform to execute stock tradingactions on behalf of the user. By way of example, the API is a set ofprogramming code that queries data, parses responses, and sendsinstructions between one software platform, i.e., system and process forsimplifying stock market based investments, and another, i.e.,brokerage.

In the context of trading, the user will invoke an API to establish aconnection between a set of automated trading algorithms and thetrader's preferred trading broker platform for the purpose of obtainingreal-time pricing data and place trades. These actions can be options,commodity purchases, derivatives, swaps, shorts, call options, futures,exchange traded funds, etc. and/or, interestingly, any combination ofany of these different trading schemes by a single user or a communityof users as described herein. As to the latter feature, the systems andprocesses described herein create different investment recipes and/orseeks a single user or a community of users to purchase a “mutual typefund” of disparate instruments based on risk factors of the individualor community.

The execution layer 325 also includes a virtual brokerage componentwhich provides the ability for a user to execute opportunities using anaccount with virtual currency with no monetary value. This virtualbrokerage account can be provided directly in the trading platform orcan be provided via the brokerage component when connecting to a 3rdparty online brokerage that offers virtual trading.

The execution layer 325 also includes bots (e.g., as described in thesection entitled “OVERVIEW OF SYSTEM AND PROCESSES (TRADING PLATFORM)”.These bots can be a brokerage bot working together with the arbiter spotnew opportunities, and submit these opportunities to the arbiter (orintelligent opportunity creator) to generate new trading recipes ormodify already existing trading recipes. More specifically, thebrokerage bots work to spot new opportunities and, in embodiments, listpossible opportunities to the arbiter. The brokerage bots can postopportunities as premium based on rules that can be easily changed inthe arbiter using a rules engine. The brokerage bots can also look attrends (e.g., trending bots) and, more specifically, what users areviewing for opportunities as well as what users are buying asopportunities for recommendation purposes. The brokerage bot can makerecommendation based current charting trends, news and other factorsthat can be filtered by the bot.

Event Layer 330

The event layer 330 is used to track user's current interactions,amongst other functionality. In more specific embodiments, the eventlayer 330 provides a scalable solution for quickly creatingrecommendations for a user by working in conjunction with the machinelearning 320, e.g., Apache Spark, to create recommendations based on howusers are leveraging their journeys, i.e., the event stream. Forexample, the streaming engine (e.g., Apache Kafka Event Stream) allowsthe machine learning 320, e.g., Apache Spark or other engine, to processsingle user event data, as well as quickly respond to larger buyingtrends by using the same entry point for all user journeys.

The event layer 330 further includes a guide manager. The guide manager(e.g., Charon) is a scalable, serverless tracker that maintains ahistory of what a user is looking at during a session to help tailorinterest to a specific risk level, sector or commodity for the user'ssession and, by using this additional information, allow the machinelearning 320 to generate or modify a preconfigured trading recipe. Inembodiments, the guide manager will help recognize changes in the user'sregular patterns and can adjust a user's trading session to account forthis change by feeding opportunities based on the change in behavior.For example, the user may typically trade in the tech sector, butsuddenly starts looking at utilities, the guide manager will recognizethis change and will understand quickly how the user is looking to tradein the new sector in order to display opportunities that meet their risklevel and sector.

The event layer 330 further includes a security layer. The securitylayer will protect mobile communications from surveillance, hacking, andinterception. In addition, the security layer can detect mobile networkinterception, as well as protect sensitive files such as proprietaryinformation. Any type of security layer can be implemented with thetrading platform and communication between a brokerage account and theuser. For example, encryption software, as well as network guards,spectrum guards and/or hardware-based secure VPN connectivity can beimplemented within aspects of the present invention. The differentsecurity protocols can be implemented by those of ordinary skill in theart without any further discussion.

Engagement Layer 335

The engagement layer 335 is used to push information to the end user.This includes chatbots or notifications. The notifications can be pushedto the user by emails and SMS, as examples. The notification can be usedto communicate the success or failure of a trade, amongst othercommunications, such as new trading recipes, brokerage information, etc.

Social Layer 340

The social layer 340 includes a leaderboard. In embodiments, theleaderboard tracks a list of most successful users and/or tradingrecipes over various periods of time such as weekly, monthly, quarterlyand yearly (or other predetermined time period) based on the percentagegains they have achieved with their picks and executions ofopportunities. The leaderboard also tracks a list of most successfulopportunities based on the summation of gains of all users that executedthat opportunity (e.g., execution of the opportunities through theirselected trading recipes). The leaderboard also tracks a list of mostsuccessful opportunity creators based on the total gains of all theusers that executed on opportunities created by that opportunitycreator. The information from the leaderboard can be used by the AI todetermine best practices, trends and different opportunities. The sociallayer 340 also includes the ability for any user of the system quicklymark an opportunity with their sentiment (positive, negative, neutral,etc.), comment on an opportunity, share it with other users, and othertypes of social interaction.

User Account 345

The user account 345 maintains a user repository including a profile,authentication data, preferences, account settings and history. Forexample, the user account 345 can maintain the profile the user such asname, ID, passwords, and other personal information. The user account345 can also store investment account information, e.g., 3^(rd) partybrokerages and other external account information, order history andrisk profiles. In embodiments, the investment account information can,e.g., name, ID, passwords, and other account information. The riskprofile can include a static or dynamic risk profile. The static riskprofile can be obtained from the investment account information;whereas, the dynamic risk profile can be obtained from the systems andprocesses described herein. The user account can also maintain aconnection profile information for the virtual broker and an executionbot. In this way, in embodiments, the user account is the masterrepository for connecting the user to the various roles from anopportunity creator to a trader.

In implementation, when a user executes a trading recipe, their accountinformation is retrieved from the user account layer 345 and the tradingrecipe is executed by the execution layer 325 against APIs exposed bythird party, external brokers. Once executed, the order history is keptin the user account 345. Also, when an investor starts to browse currentrecipes, the user account layer 345 is used along with the machinelearning 320 to suggest specific recipes stored in the storage layer tothe investor.

Micro-Services Layer 350

The micro-services layer 350 provides a granular approach to surfacingRestful web services to end user channels. As the systems and processesdescribed herein (e.g., trading platform) leverages an omni-channelapproach to displaying and/or communicating information to the end user,e.g., investor, opportunity creator, etc., it is useful that theservices can be easily ingested across mobile, web, chat or otheremerging interface. The micro services layer 350 lets the developer,creator, etc., decide which channels to expose quickly without having tocreate a service logic layer for each channel.

Query Layer 355

The query layer 355 provides graphing and data visualizer to the enduser. For example, the query layer 355 includes Candela which is anopen-source suite of interoperable web visualization components. Thequery layer, e.g., Candela, makes scalable, rich visualizationsavailable with a normalized API for use in real-world data applications.See, e.g., Candela (https://candela.readthedocs.io/en/latest/, Copyright2016, Kitware, Inc., Revision 51d7b2b9). It should be understood bythose of skill in the art that the systems and processes describedherein can also use other graphic tools for charting, graphing orproviding other visualizations, e.g., recipes, using the data andanalytics described herein, e.g., Query and visualize Presto databasedata with Holistics SQL editor and visualization tools.

Cookbooks and Trading Recipes

There is a considerable amount of information a user needs to understandbefore buying a contract or other complex trading opportunity (e.g.,option to buy 100 shares of stock). For example, executing an optionsbuying pattern is confusing since the user needs to understand thestrike prices, the number of contracts available and filter the optionbased on time decay or how close to the money the option is. And, byreceiving a recommendation for a specific play from an advisor over anemail or trading engine, the suggested strike price may not be availableto the investor when they go to execute the play (trades). Also, mosttrades consist of multiple purchases of buys and sells of calls andputs. This means the user actually needs to make multiple, manual tradesat human speed to make sure they can execute their play.

In this example, the user of the trading platform does not need tounderstand the complexity of buying and selling options, commodities,derivatives, and/or other trading opportunities such as money exchanges,etc. The user simply reads about a particular trading recipe associatedwith, e.g., the Acme Beverage Company upcoming earnings call, anddecides if they would like to invest by selecting between the variousfixed trading recipe scenarios of +4%, +2%, −2% and −4% as generated,created and presented as an opportunity with execution recipes for eachof the available scenarios by the systems and processes describedherein.

The Trading Recipe

In embodiments, the systems and processes described herein, e.g.,trading platform, provide a recipe template catalog 400 as shown in FIG.4. The recipe template catalog 400 can be generated by the architecturalenvironment shown in FIG. 3.

In embodiments, the recipe template catalog 400 provides a guide or atemplate where the intelligent opportunity creator creates newopportunities or modifies existing opportunities in the form of atrading recipe. For example, a Bull Put Spread is an example of a stockoptions strategy to invest in the underlying stock with the outlook thatit will increase in value. The strategy involves buying a Put Option ata certain level and then selling a Put Option at a higher level. Inaddition to these two Put Option prices, the user also needs to know theoptions expiration date and the limit price for buying or selling theoptions. It may also be necessary to know the stop price where theinvestment should be automatically liquidated because the investment isgoing in the wrong direction. Other Options strategies such as IronCondor or Butterfly Spreads require addition parameters. As complex asthese trades can be, the trading recipe templates will capture thesedifferent types of strategies along with the required parameters forexecution by the investor. This simplifies the execution strategies ofthe investor by using a single trading recipe that can be executed witha single action because all the steps are included in the tradingrecipe. In addition, the trading recipe can be provided in differentrisk profiles for the same type or kind of trade, thereby allowing theinvestor to best match their risk tolerance with a specific opportunityusing a single trading recipe.

Referring again to FIG. 4, the recipe template catalog 400 includesmultiple recipe templates 405 each of which may represent a complexinvesting strategy comprising different opportunities. For example, sometemplates are regular trading patterns known in the industry, but othertemplates are customized templates to simplify investments strategiesinto the trading recipes as generating using the many external andinternal factors described herein. The recipe template catalog 405 canstore the recipe templates 405 which are used to create trading recipes410, 415. The recipe template catalog 405, recipe templates 405 andtrading recipes 410, 415 can be stored in the storage system 120 (e.g.,opportunity registry) of FIG. 1.

Once the recipe template 405 is completed with specific parameters ofthe investment then a new trading recipe 410 can be created which isexecutable by multiple users. For example, in embodiments, users of theplatform view the created trading recipes as part of prepackaged tradingopportunities which can comprise one or more series of complex trades,where the underlying recipe 410 is how the creator recommends executingon that opportunity. The trading recipes 410 can be viewed on a displayusing, e.g., query layer 355, shown in FIG. 4, and then executed uponusing the architectural platform of FIG. 3. The user can view and evenmodify the trading recipe, if desired, before executing on theopportunity. Once the trading recipe is executed, (e.g., using theexecution layer 325 of FIG. 3, it will be transmitted to the brokerageaccount and executed as an investment for the user.

In embodiments, the intelligent opportunity creator that creates thedifferent recipes is artificial intelligence as described in the sectionentitled “OVERVIEW OF SYSTEM AND PROCESSES (TRADING PLATFORM)”, andwhich uses the architectural environment as described with respect toFIG. 3. Moreover, any user can act as a creator by creating or modifyingtrading recipes which are then consumed or executed by the investor. Theinvestor, for example, can also execute as creators by providingadditional comments or ratings on an opportunity or creating variationsof the underlying trading recipes that act on the same underlying stockor other trades, but uses different parameters to modify the risk/rewardof the original recipe or could also modify the recipe to only increasein value of the underlying stock moves in the opposite direction assumedin the original recipe.

In exemplary cases, a trading recipe 410 can be any type of tradingtype. For example, a trading recipe 410 can be used with Call Optionsand Put Options to leverage the amount of money used when executing thetrading plan associated with the recipe. This enables the user to use asmaller monetary investment in the trading recipe 410 and receive alarger gain on that amount compared to the movement of the underlyingstock value. For example, the user could execute a recipe using $100 anddouble that amount if the expected outcome occurs even though theunderlying stock only goes up by a single digit percentage gain.

In embodiments, as users interact with the system, the users can changepersonas between creator, contributor and consumer of opportunities,i.e., traders. Also the trading recipe variations can occur after thetrading recipe 410 has been executed and is an active and openinvestment. For example, an executed trading recipe may have included anautomated exit at 50% gain. The creator of the trading recipe 140 oranother contributor can update the trading recipe 410 or create a recipevariation to modify certain characteristics of the trade that iscurrently active.

In embodiments, the trading recipes 410 can be organized into one ormore cookbooks 415. For example, the cookbooks 415 can be created withtrading recipes that are high risk, low risk, bullish, bearish, or basedon types of investments such as technology, medical, commodities,currency valuations, etc. This allows users, i.e., creators, traders,etc., to subscribe to a specific cookbooks of interest in order toexpedite finding of certain types of trading recipes.

Example Interfaces of Trading Recipes

An example trading recipe is shown in FIG. 5. It should be understood bythose of ordinary skill in the art that the interface, e.g., asimplemented in the computing infrastructure of FIG. 1, can be providedby as a standalone system or implemented directly into a brokerageaccount. Also, the interface, which can be any graphical user interface,can be implemented in a mobile device or other computing system.Moreover, the interface can be provided by the query layer 355, as anon-limiting illustrative example, of FIG. 3. It is further understoodthat the trading recipe of FIG. 5 is only one type of scenario usingsecurities, and that similar information can be provided for othertrades, e.g., futures, derivatives, commodities or other types oftrades. And, by provided the trading recipe as described herein, theuser no longer needs to understand the complexity of buying and sellingoptions, commodities and/or other trading opportunities.

In the example of FIG. 5, stock options are used; although examples areapplicable to other trading strategies as should be understood by thoseof skill in the art. In any event, with options, the amount of moneyrequired to execute on the opportunity is much lower and the amount ofgain is much higher. The difference is that the price paid for theoption is not recouped and has to be subtracted from the gains tocalculate the profit. Buying stock options is done by buying ‘contracts’which is the agreement to buy or sell a stock at a specific price.

In FIG. 5, the interface 500 shows the following textual display, wherethe investor can select a desired outcome of +4%, +2%, −2% or −4% basedon risk tolerance.

Big Swing in Stock ABC after Earnings

-   -   Acme Beverage Company (ABC)'s stock is currently trading at $100        per share and has had big swings of more than 5% after each of        their past earnings announcements. Some analysts are expecting        them to significantly beat earnings expectations due to lower        supply chain costs as seen in similar companies that have        already reported. Other analysts are expecting a big miss due to        higher labor costs in states where the majority of their        distribution centers are located. Earnings will be announced on        Thursday at 4:30 pm Eastern Time. The Opportunity is to buy        options prior to the earnings call on Thursday for either a        stock increase or stock decrease and then close that position on        Friday at 10 am.

The user (investor) can select a desired outcome of +4%, +2%, −2% or −4%based on risk tolerance. By selecting the desired outcome, the systemsand processes will then provide a trade execution display based on theselected trading recipe as generated by the systems and processesdescribed herein, with particular information required by the user,e.g., number of investments.

The trading recipe shown in FIG. 5 is based on a +4% desired outcome,e.g., high risk. More specifically, the trading recipe 500 includes:

RECIPE 1: Opportunity Fixed Execution 1—Stock Increase of 4+% afterearnings

Trade: Stock ABC is currently trading at 100 dollars per share. Thistrade will buy a Call Option with a strike price of $102 that expires onFriday at a cost of $0.75 for the Call Option expecting the stock willjump to $104. Each $75 invested for a set of 100 contracts will bevalued at $200 for a profit of $125 (167%) after subtracting the priceof the options contracts.

Formulas:((<Current Stock Value>−<Option Strike Price>)*<Number ofContracts>)−Price for Options)=Profit(($104−$102)*100)−$75=$125Profit/Cost=Percentage Gain$167/$75=167%

It should be understood, though, that the investor can also select theother risk tolerant trades, which are generated into different tradingrecipes for execution by the investor. By way of example:

RECIPE 2: Opportunity Fixed Execution 2—Stock Increase of 2+% afterearnings

Trade: Stock ABC is currently trading at 100 dollars per share. Thistrade will buy a Call Option with a strike price of $101 that expires onFriday at a cost of $0.90 for the Call Option expecting the stock willjump to $102. Each $90 invested will return $100 for a profit of $10(11%).

RECIPE 3: Opportunity Fixed Execution 3—Stock Decrease of 2+% afterearnings

Trade: Stock ABC is currently trading at 100 dollars per share. Thistrade will buy a Put Option with a strike price of $99 that expires onFriday at a cost of $0.87 for the Put Option expecting the stock willdrop to $98. Each $87 invested will return $100 for a profit of $13(15%).

RECIPE 4: Opportunity Fixed Execution 4—Stock Decrease of 4+% afterearnings

Trade: Stock ABC is currently trading at 100 dollars per share. Thistrade will buy a Put Option with a strike price of $98 that expires onFriday at a cost of $0.79 for the Put Option expecting the stock willdrop to $96. Each $79 invested will return $200 for a profit of $121(153%).

In this example, the user does not need to understand the complexity ofbuying and selling options, commodities, derivatives, and/or othertrading opportunities such as money exchanges, etc. The user simplyreads about the opportunity associated with, e.g., the Acme BeverageCompany upcoming earnings call, and decides if they would like to investand chooses between the various fixed recipe scenarios of +4%, +2%, −2%and −4%. Once the selection is made, an execution bot will execute thespecified options trades via the broker integration component of theexecution layer 325, as soon as the user makes their selection. Theexecution bot will update the user's account with the status of thetrade while it is active and then close it at the configured time basedon the recipe execution plan. Each recipe is displayed to users in asimple format allowing them to quickly review the details of the recipeand make their selection on their expectation of the outcome.

Referring still to FIGS. 3, 4 and 5, one or many uses of the recipetemplates and collection of completed recipes in a user's one or morecookbooks is to provide input for the machine learning system to producenew recipes as shown in FIGS. 4 and 5. The machine learning 320 usesuser patterns, data from the content feeds 310, and templates forcreating trading recipes. The machine learning 320 is further configuredand structured to provide recipes based on, e.g., (i) values enteringinto the templates to create actionable recipes, (ii) what recipes arepopular and being executed the most, and (iii) which recipes are beingsuccessful at generating gains, etc.

In embodiments, the machine learning 320 needs to only understand whento apply a specific pattern for a given situation. For instance, if themachine learning 320 determines that there is a good chance (e.g.,greater than 50%) a stock is going up in the near future based oncurrent market trends, social media traffic and past stock trends for aspecific stock, then the machine learning 320, acting as the intelligentopportunity creator, can use a template to generate variousopportunities based on this outcome. As the machine learning 320improves, it may pull past recipes and reuse them, just as it would atemplate in the cookbook.

Creating a Trading Recipe

A trading recipe is a complex process due to the fact that data needs tobe constantly ingested by the trading platform. The systems andprocesses manage this ingestion by scaling the ingest of data fromvarious sources into the trading platform. Once this is ingested, it canbe used by the machine learning 320 using, e.g., past history ofcommodities, market movements, past recipe data, investor sentiment, andeven past user account data to create the trading recipe, using thearchitecture as already described in FIG. 3 and the systems shown inFIGS. 1 and 2, as examples.

FIG. 6 shows a subset of components shown in FIG. 3 and theirinteractions in accordance with aspects of the present disclosure. Morespecifically, FIG. 3 shows the arbiter 320 b of the machine learningcomponent 320 reacting to increased traffic from a stream, where thearbiter notices increased traffic from Apache Spark 320 a (machinelearning) during processing of incoming data (e.g., from the contentfeed 310). In embodiments, the arbiter 320 b will fire off multiple“workers” (e.g., bots) to apply the different algorithms 600 to thetrading recipe. By way of example, each algorithm can return a score byrunning through a neural net, and the raw output from the final runs ofthe neural net is a confidence score that can be used (can be apercentage of confidence) by the arbiter with other scores fromalgorithms to determine if a trade is necessary (as described in detailherein, where reference can be made to FIGS. 13 and 14, amongst otherdescriptions herein).

When the arbiter 320 b notices high traffic for a particular stock, highactivity or cost for certain options on a stock, or other types oftrades, as an example, it sends a request to run the algorithms 600 forthat particular trade and rate the responses to determine the besttrading recipes. In embodiments, each algorithm 600 returns a score forhow likely it thinks a recipe or recipes will be effective as shown,e.g., in FIGS. 13 and 14. The arbiter 320 b can take the ratings andchoose one more recipes to apply.

By way of example, following the idea of a particular stock associatedwith company “X” example, the arbiter 320 b detects that there is a lotof traffic for the company “X” in the news and via the market. Theoverall news sentiment is determined to be positive, with the stocktrending positive. In this example, the arbiter 320 wants to determinethe best recipe to offer the user of the trading platform.

In this case the machine learning 320 leverages Apache Ignite forgenetic algorithms. In this system, each basic template in the cookbookis defined as a Gene. In a genetic algorithm, a gene is an optimalsolution. Therefore, for each template, the machine learning defineswhen to optimally apply the options pattern or other pattern to theingested data. A fitness score is used to determine how optimal thesolution is relative to other potential solutions in the population.

The problem can be run using multiple genetic algorithms for each of thetemplates to determine which template should be applied to the problemby recommending the template with the highest fitness score. Forinstance, stock “X” is at $55. In this example, the social sentiment ispositive and the news sentiment is positive. The machine learning 320,e.g., Apache Spark 320 a, runs each of the positive play recipe'sgenetic algorithms for fitness. The fitness algorithm yields a score foreach recipe, where each recipe can be run on its own and is independentof the others due to the fact that the trading platform described hereinuses a distributed machine learning system. Once the arbiter 320 breceives all the results, it can choose the best recipe or recipes basedon the higher scores returned from each genetic algorithm ran.

In further embodiments, a creator (intelligent opportunity creator orthe opportunity creator) could send a request to the arbiter 320 b witha suggestion, e.g., stock “X” will increase in price. The arbiter 320 bcan run the genetic algorithms and suggest a particular trading recipeto the recipe creator as a starting point. This allows the creator toassist machine learning in cases where the creator may understand anaspect of a stock not understood by the trading platform, giving thecreator insights while providing a best fit recipe if the stock doesrise. In this case, the arbiter 320 b returns a set of suggested tradingrecipes, and the opportunity creator or other user can accept, edit orreject them instead of them instantly being placed into the recipestore.

Tailoring Recommendations

Building on the example of FIG. 6, FIG. 7 shows a subsystem to tailorrecommendations for trading recipes. By way of example, when a userbegins transacting with the trading platform, a guide manager 700 (e.g.,Charon) is instantiated for the user. The guide manager 700 is adedicated digital manager for the user as long as they are active on thetrading platform. Once the user's session time's out, e.g., 15 minutesof inactivity, the guide manager 700 is destroyed.

In embodiments, the guide manager 700 tracks the recent history of theuser. For instance, if a user looks up a series of utilities and/or highrisk investments, the guide manager 700 will track that they arecurrently interested in high risk utilities. This is important as thetrading platform not only can make recommendations based on pasthistory, but recommendations based on what a user is doing during hiscurrent session. The guide manager 700 can thus be used to tailorrecommendations for recipes by tracking the stocks or other tradingtypes the user is looking at, the industry, risk level, and theinformation they are reading from the news feeds, etc., and provide thisto the intelligent opportunity creator for consideration when generatinga trading recipe.

Once the arbiter 320 b establishes that the user is viewing a particularcommodity or sector, the guide manager 700 checks the user account todetermine if this is a regular pattern or a new pattern for thissession. Since the trading platform can already deliver recommendationsbased on the user's past history, the guide manager 700 does not need torequest new recommendations based on the user's account profile.Instead, the guide manager 700 will focus on the user's current session.Once the guide manager 700 determines that the user is looking atcommodities, sectors, trading types, etc., outside their normal tradingpattern, the guide manager 700 will want to receive recommendations fromthe arbiter 320 b based on the current session, which will then beprovided to the intelligent opportunity creator for consideration whengenerating a trading recipe.

Illustrative Swim Lane Diagrams Implementing Functionality/Flows of theTrading Platform and Components Thereof

Aspects of the present invention are described herein with reference toflowchart illustrations (swim lane diagrams) and/or the block diagramsof methods, apparatus (systems), and computer program products. It willbe understood that each block or process of the swim lane diagramsand/or block diagrams, and combinations of blocks in the swim laneillustrations and/or block diagrams, can be implemented by computerreadable program instructions as described herein. These computerreadable program instructions may be provided to the processor 115 ofthe computing system 100.

Moreover, the swim lane diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods, and computer program products according to various embodiments.In this regard, each block in the flowchart may represent a module,segment, or portion of instructions, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). In implementations, the functions noted in the steps mayoccur out of the order, depending upon the functionality involved. Itwill also be noted that each step of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

Execution of Trading Recipes

FIG. 8 shows a swim lane diagram of an exemplary process in accordancewith aspects of the invention. As shown in FIG. 8, several actors areincluded in the implementation of this illustrative example. Theseactors include: an investor, a guide manager (Charon), an arbiter,machine learning, a recipe store (repository or storage system) and auser account (account).

Generally, the processes of FIG. 8 include an investor looking atspecific trading types. The Charon notices the user keeps browsing thesame sector and queries the user account to see if this is part of theinvestor's regular profile. If it is not, it is determined that theinvestor is trying something new in this session or onto a lead outsidethe normal buying sector. The Charon then sends a request to the arbiterto make a new recommendation. The arbiter checks the current recipestore for any running trading recipes. If one exists, it is sent to theuser. If one does not exist, a new trading recipe can be created asdescribed herein.

More specifically, in FIG. 8, at step 800, an investor will begin aparticular trading type. This particular trading type can include, e.g.,buying a particular stock, watching market fluctuations, research, etc.,In doing so, the investor will interact with the Charon. At step 802,the Charon will detect a pattern and, at step 804, query the accounts(user account) for a profile of the user. At step 806, the account willprovide a profile to the Charon.

At step 808, the Charon will query the arbiter to find recommendations.In embodiments, the arbiter can also query the current Charon to lookfor trends among different investors. In this scenario, the arbiterqueries all Charons looking for a trend among all or any combination ofcurrent users. For instance, the Charon sees that 33% of users arelooking at the stock “X”. This can be determined a significant number ofusers, in which case the arbiter can then determine if there are recipesfor stock “X”.

At step 810, the arbiter will determine a position type (e.g., type oftrade, research or other associated information) and, at step 812,request a trading recipe from the trading recipe store (e.g., reciperepository). In embodiments, as with a personalized trend, either theCharon or the arbiter can check the current recipe store. If a recipeexists, there would be no need to create a new recipe in real-time and,at step 814, the arbiter will receive the trading recipe from the recipestore. If a recipe does not exist, the arbiter can view current marketsentiment, news sentiment and market trends and/or other content feedsto make a recipe hypothesis then submit the hypothesis to the machinelearning (e.g., machine learning agents). As with personalizedrecommendation scenarios, the agents return recipes and ratings forthose recipes back to the arbiter. The arbiter then adds a new recipe orgroup of recipes to the recipe repository and then send therecommendations to the Charon to present as a recommendation back to theuser.

In either of the case of a new or existing trading recipe, at step 816,the arbiter will request execution of the recipe using the machinelearning, e.g., execution bots. At step 818, the machine learning canprovide a recipe score to the arbiter (and/or build a new recipe). Thearbiter, at step 820, will determine the best trading recipe based onthe information obtained, e.g., using the data of a content feed and/orother internal factors such as a user's trade history and/or profileand/or sentiment. At step 822, the arbiter provides the recipe score tothe Charon which, in turn, sends the score to the investors at step 822.

As noted in FIG. 8, the steps 808 to 822 are performed outside of theprofile. The steps 816 to 822 are provided in real-time and can beprovided in a looped format for each learning agent of the machinelearning.

Artificial Intelligence Assisted by a Creator

FIG. 9 shows a swim lane diagram of an exemplary process using anartificial intelligence assisted by a creator in accordance with aspectsof the invention. As shown in FIG. 9, several actors are included in theimplementation of this illustrative example. These actors include: acreator (opportunity creator or contributor), an arbiter, machinelearning (intelligent opportunity creator) and a recipe store(repository or storage system).

A component and capability of the trading platform is the ability of aninvestor or other user to approve a recipe as well as suggest analternative recipe, including a bet against the current trading recipe.In this exemplary illustration, part of creating recipes is generatingideas, which can be assisted with the help of a user (e.g., contributor,commentator, investor or other third party) finding new ideas fortrading recipes and allowing the creator to suggest a tradingopportunity or modification of an existing trading opportunity to thearbiter.

In any of these different scenarios, a user (generally in this sectionas a “creator”) can comment on a trading recipe and suggest anadjustment to the trading recipe by cloning the trading recipe. Whenthis happens, the creator can make a change to the trading recipe andprovide a reason why they made such a change. The creator can alsorecommend a different trading recipe with a similar result. Forinstance, several persons can believe a “stop” will go down, but thecreator thinks a bear call spread is a better play than the currenttrading recipe, in which case the creator can provide a new play (e.g.,revise or create a new recipe or part of a recipe).

Also, the creator may disagree with the trading recipe recommendationall together. In this case, the creator can link an existing, opposingtrading recipe for the particular recommended trading recipe or they cansuggest an entirely different trading recipe. In these cases, thecreator can be guided through a recommendation via a wizard. Forinstance, if the creator thinks there is an opposing play to a Bull CallSpread, they may want to recommend a different play. In this case, thetrading platform will recommend an opposing play to the current play ora group of opposing plays. For instance, the trading platform wouldrecommend a Bear Call Spread as the natural opposing play, but can alsolist other similar plays that oppose the Bull Call Spread, includingmore interesting plays, such as a short put butterfly spread that sellsmultiple puts on different time frames. The machine learning (as arecommendation engine) can even help the contributor setup an IronCondor play if they think stock will be neutral. The wizard can alsodetermine if the user needs a play, like a strangle if the user believesit will go very up or very down, but does not know which.

The arbiter can use the recommendation and return a recommended set oftrading recipes based on the creator's (or other user's) suggestionabout a sector, commodity, risk level etc. Accordingly, unlike journeymanaged trends, the creator can override market sentiment, socialsentiment and news sentiment based on their own knowledge. This allowsthe creator to give a neutral or positive sentiment for a particularfactor, such as social sentiment or market sentiment if they arepredicting a piece of knowledge that will happen. For example, a creatormay think that the upcoming ad during a major sporting event will drivesales of a certain product, so they may adjust the social sentiment aspositive (from neutral) for their recipe. In this case, the creator canoverride sentiments when they ask the arbiter to look forrecommendations.

More specifically, in FIG. 9, at step 900, a creator (e.g., investor orother user) can provide a suggestion to the arbiter. The suggestion canbe a sentiment or other data point as described herein. At step 902, thearbiter forms a hypothesis overriding the current sentiment of thecommodity, sector or other trading type with the creator's sentiments(or other data point). The suggestion is then provided to the machinelearning at step 904, which will then create a rating and return therating (score) at step 906. At step 908, the arbiter will use the scoreand determine the best recipe for the user. The recipe can be a revisionof an existing recipe or a completely new recipe as described herein. Atstep 910, the recommended recipe will be provided to the creator. Thecreator has the option of editing the recipe at step 912. At step 914,the creator can submit the newly formulated recipe to the recipe store.

Account Brokering

FIG. 10 shows a swim lane diagram illustrating a trading recipe beingexecuted using a broker bot to a specific broker in a single commoditytransaction in accordance with aspects of the present disclosure. Asshown in FIG. 10, several actors are included in the implementation ofthis illustrative example. These actors include: an investor, a brokerbot (opportunity execution bot), a user account (account), a recipestore (repository or storage system), an order history and a broker.

In this implementation, a user can submit an order to an outside brokerusing the trading recipe. When the user places an order, the broker botis instantiated, which is designed to communicate to the specificbackend broker, i.e., brokerage account of the user. This allows thetrading platform to instantiate the broker bot specifically designed tomanage transactions between the outside broker and the trading platform.Accordingly, in this example, a recipe can be executed using a brokerbot to a specific broker in a single commodity transaction. It isimportant to note that the bot is serverless or computer agnostic. Inbasic, the broker bot handles the purchasing transaction for theinvestor. Since each broker has different transaction flows between thebroker and the bot, the use of a bot makes maintaining the code to thetransaction easy and isolated from other brokers. This also allows thetrading platform to place commodity orders to two different brokerssimultaneously and asynchronously, but put the order back together aspart of the order history to track the transaction across multiplebrokers.

More specifically, in FIG. 10, at step 1000, an investor will select andbegin a particular trading opportunity by executing a recipe, using anopportunity execution bot (broker bot). At step 1002, the broker botwill query the accounts (user account) for a profile and/or otherbrokerage information of the user. At step 1004, the user account willprovide the brokerage information to the broker bot. At step 1006, thebrokerage bot will send the recipe (e.g., trading information) and userinformation to the broker for execution of the recipe. At step 1008, thebroker will sent a confirmation order to the broker bot. At steps 1010and 1012, the investor and broker bot will confirm the orders. At step1014, the broker bot will confirm the execution of the order with thebroker and, at step 1016, the broker will send a complete message to thebroker bot.

At step 1018, the broker bot will send the recorded transaction to theorder history. In embodiments, the recorded transaction in the orderhistory can saved as a recipe. At step 1020, a confirmation that therecorded transaction has been received (and saved) is sent from theorder history to the broker bot. At step 1022, the broker bot canprovide an adjusted risk profile to the user account based on theexecuted transaction. The adjusted risk profile is based on, forexample, the recently executed transaction. At step 1024, the process iscompleted.

In embodiments, any trade may include a risk profile. This means thatthe recipe has a risk rating or very low, low, medium, high, extremelyhigh, speculative. Each of these trades has a numeric value associatedwith the risk. For instance, the internal risk number for very low maybe −50 while medium may have a numeric value of 0, and a speculativetrade may have a value of 50. When a user executes a trade, the riskprofile engine will adjust the user's risk profile by the numeric valueof the trade using the following formula.CurrentRiskRating+(RecipeRisk+Riskfromlast10trades/11)

This formula creates a lesser effect of trades over time. If the user'strading patterns change over a series of trades, the effect is weightedoverall. With a minimum risk of −1000 maximum risk of 1000.

Cross Commodity Suggestions

In further implementations, a user can search for mixed recipes thatcombine stocks, commodities, options, or any host of assets together asa larger trade that can span multiple markets. For instance, a userwants to invest in a big oil play where an oil tanker of a particularcompany exploded. Because of this, oil futures will go up, but the usermay want to buy an option for a particular company with an option for anoil future. For this scenario, the machine learning will understand therelationship between commodities.

More specifically, the rules engine of the machine learning, forexample, can assist in creating cross-market recipes that can coverinterrelated commodities. In the example above, there is a relationshipbetween oil and an oil company “Y”. The machine learning can define thatcompany “Y” is in a specific sector or sectors, and define commoditiesassociated with the sector. In this case, the Sector is Oil and Gas, thecommodities are Oil, Gas, and companies “Y” and “Z” are associated withthe section. The rules engine of the machine learning can storeconfigurations on commodities and sectors in the storage system e.g.,storage system 120.

The two JSON object below show simple example configurations.

  Sector:{   “Commodities”:[{          “Name”:”Exxon”         “Type”:”Stock”,          “Market”:”Stock”},         {         “Name”:”Oil”          “Type”:”Commodity”,         “Market”:”Futures”         },   ]   “Stock”:{      “Sectors”:[“oil”]       “Commodities”:[{          “Name”:”Toyota”         “Type”:”Stock”,          “Market”:”Stock”        },   ]

The sector example shows how to tie stocks in a sector with commoditiesnormally traded in that sector. It should be understood, though, thatnot all sectors will have a commodity accordingly, the second exampleshows how a stock can be a member of a sector, but it also shows that astock can have commodities from a sector or commodities specific to thatstock that may be outside the sector.

Sentiment Overview

Sentiment serves a role in determining whether to invest in something,and the effect of these sentiments play differently on different tradingopportunities, e.g., options trading versus value investing. For thisreason, the processes and systems use sentiment analysis to formulate aparticular trading opportunity by ingesting, determining and/orimplementing whether a piece of writing is positive, negative or neutralbased on the attitude of the speaker, and using this information as afactor in generating a trading recipe to execute on that investmentopportunity. By looking at sentiment from different factors, it is nowpossible to determine success factors in buying or selling certaintrading opportunities and use this information when generating thetrading recipe and using the various sources of sentiment as part of theopportunity package to explain the rationale for a recipe that trades anunderlying stock, option, etc. as increasing or decreasing in value thatassists the user (investor) in making a decision in executing on thatopportunity via a specific recipe.

In embodiments, the systems and processes leverage several types ofsentiment including, social sentiment, management sentiment, investorsentiment, and professional sentiment. These sentiments can be obtainedby providing links to positive and negative articles about a particulartrading opportunity. These links can be the news feeds 310 describedabove. In the end, each type of sentiment yields a positive or negativesentiment of the subject which, in turn, is used as part of a neural netto determine what patterns to apply to an investment and how toformulate or generate a particular trading recipe for execution by theinvestor.

Social Sentiment

Social sentiment measures how people on social networks, e.g., Twitter®,Facebook®, and other social media outlets, speak about a stock or othertrading opportunity. The systems and processes herein can learn ondatasets for words such as, e.g., dislike, stock tanks, hate, etc. toprovide a sentiment rating for the trading opportunity and, taking intoaccount the rating, generate different risk profiles of particulartrading recipes, as an example. In this way, social sentiment can informthe systems and processes how the general population feels about acompany and/or its products and use this sentiment as a factor ingenerating the trading recipe as described herein.

Management Sentiment

Management sentiment teaches how people feel about the management of acompany. Management sentiment is usually pulled from various sourcesincluding web articles, social sites and professional sites. This datacan commonly be pulled from stock lookups, and used as part of thedatamining analysis of a company's management. To use managementsentiment, the present solution leverages training of a dataset basedoff different sources to determine positive and negative analysis ofmanagement given a determination of negative and positive stories onmanagement itself. The solution can also take into account members of acompany's management team.

Management analysis can be used in short term and long term trading toshow the effect of stable management to whether a bad hit versus poormanagement. In an example, a company's CEO has continually invested cashflow back into the business for years and is evident by many of articlespublished about the CEO during his tenure at such company. In manycases, people bought into this company for the management andinnovation. This is important for many stocks, because, if themanagement style is perceived as negative, then the stock will havelarger drops and lends itself to strategies that profit from this ratherthan a strong management that may be able to buffer, even a short termnegative news cycle with little loss in stock.

Accordingly, by using management sentiment, it is now possible todetermine if a company is playing follow the leader or innovating, bykeying on words on how management is investing their cash flow, etc. Forexample, if a management team is pushing profits and lowering investmentin future growth, it can be used in a sentiment analysis to show thatthe company's stock may be inflated temporarily. This sentiment can thenbe a factor in generating the trading recipe as described herein.

Professional Sentiment

Professional sentiment is derived from viewing overall sentiment ofleadership from news articles and professional services, as examples.Professional sentiment is also easily obtained via sentiment analysisaugmenting the analysis with the recommendation to buy, hold or sell, asexamples.

Implementation of Sentiments

Obtaining sentiment across different groups or platforms is known, butunderstanding how to apply different groupings of sentiment into afinancial equation has been difficult to implement. For example, it isdifficult to determine how important social sentiment is to aninvestment in the short term or long term, or how a certain sentimentshould be weighted, e.g., management sentiment on short term and longterm investing, etc. As multiple sentiments are not applied to investingtoday, investors do not currently leverage a multi-sentiment neural netdescribed herein to create stock and options investments, as well asother trading opportunities. To solve these problems, the presentapproach looks at key variables for short term and long term investingas well as the sector, and applies sentiment based analysis acrossmultiple sentiment groups, balancing the impact of these groups to makefuture predictions about stocks, options or other trading opportunities.

As shown in FIG. 11, for example, sentiment is run for each stock (orother trading opportunity) using sentiment analysis for each of keysentiment group. In the example shown in FIG. 11, a sentiment for aparticular stock is provided; although, a similar analysis can be usedfor other trading opportunities. In this example, at step 1102,financial data is retrieved to determine sentiment.

In embodiments, each sentiment (e.g., management sentiment 1200,investor sentiment 1202 and social sentiment 1204) takes input frommultiple sources 1206 as shown in FIG. 12. For example, as shown in FIG.12, some of the sentiments share data sources, such as Twitter®,Facebook® and other financial sources (e.g., Morningstar®). This ispermissible as each sentiment tool measures sentiment based on adifferent dictionary (different datasets). For example, the managementsentiment is looking for what professionals, investors and other thinkabout the management for the company. So, the keywords would includemembers of the management team along with keywords like leadership.

At step 1104, the stocks are divided into sectors. Once the stocks (orother trading types) are divided by sector, the algorithms (e.g.,machine learning 320) are split by the type of investment: short termstock, long term stock, option, etc. At step 1106 a neural net is runfor each sector and, at step 1108, a sentiment is predicted for eachstock in each sector.

FIGS. 13 and 14 show the complexity of creating filtered trades thatmatch a trader with recommended trades. More specifically, FIG. 13 showsinputs for triggering a neural network and FIG. 14 shows an exemplarytraining of a neural network, each of which is in accordance withaspects of the present disclosure.

As illustrated in FIG. 13, as with any deep learning solution, thesolution can now be trained with data by looking at past data, the keymetrics and sentiments and the outcome of the investment (e.g., did itdo up, did it go down). Regardless of the type of investment strategy,the platform described herein will leverage a learning mode. This isbecause a machine learning solution cannot predict on things it does notknow about. Therefore, training different layers of neural network isfirst performed. This training can be ongoing, which will make thealgorithms more robust. In the training, each of algorithms ingests datato tell if the outcome was a gain in price of the stock or a loss orother trading criteria. At this point, there is no concern about profitor loss because the platform will leverage the fact the stock moves upor down to the user's favor in most commodity trading systems, such asstock or options.

In the first layer of our function, the neurons leverage the inputs fromthe financial data and determine based on the weights of each input ifthe neuron should fire. In the first layer, for example, the neuralnetwork may just look if the stock went up or down based on out data.The example shows a series of financial inputs used in short and longterm stocks investing, wherein each one of these values has a numericrepresentation, e.g., Book to Price is 5.19, Earnings per Share is 46.6,return on assets is 8.85, etc. In the example, there are 11 inputs intothe neural net; although any amount of inputs are contemplated herein.Additionally, investor sentiment can be used as determined fromperforming a sentiment analysis for the investor, e.g., rated at 56%Social Sentiment was 78% positive etc.

The example of FIG. 13 shows a series of financial inputs 1300 used inshort and long term stocks investing. In the example of FIG. 13, thereare 11 inputs 1300 into a neural net 1304; although any number areinputs are contemplated herein. In the example of FIG. 13, for example,each one of the inputs 1300 is weighted with a numeric representation,i.e., company X has a Book to Price of “w/4”, etc. Additionally, aninvestor sentiment 1302 can be determined from performing a sentimentanalysis, which can be rated a positive, negative or neutral, etc. Thesevalues are fed into the neural net 1304 to determine whether theinvestment should or did go up or down, e.g., neuron will fire 1 if thestock went up and a fire 0 if the stock went down.

Referring to FIG. 14, these values are fed into the neuron. In the firstlayer, it is just necessary to know if the investment went up or down,so the neuron will fire “1” if the stock went up and a “0” if it wentdown. Once it is determined if a stock went up or down, the layers afterare used to determine what percentage is expected for the stock to go upor down based on the inputs. As with any neural network, the network istrained with back data, including past sentiment for each sentimentgroup as well as pulling in historical data that includes the percentagethe stock went up or down for training. And, looking at the illustrationof FIG. 14, the neural network will take in the financial and sentimentdata in layer 1 and then using the hidden layers it can predict anoutcome from the training data used to create a percentage gain, forexample.

By way of another example, as with any deep learning solution, thesystems and processes are trained with data by looking at past data, keymetrics, sentiments and, for example, outcome (e.g., of the investment).Accordingly, regardless of the type of investment strategy, the systemsand processes leverage a learning mode to train different layers of theneural network. For example, referring to FIG. 14, in the first layer,for example, neurons leverage the inputs from the financial data such asshown in FIG. 13. In the first layer of FIG. 14, for example, thesystems and processes can look to see whether the stock went up or downbased on analyzed data shown in FIG. 13. More specifically, the neuralnetwork will take in financial and sentiment data in layer 1 and thenprocess data through the hidden layers 2-4 until the predicted outcomefrom the training data can be used to create a percentage gain or losson a stock based on the financial and sentiment data.

In embodiments, a service provider could offer to perform the processesdescribed herein. In this case, the service provider can create,maintain, deploy, support, etc., the computer infrastructure thatperforms the process steps of the invention for one or more customers.These customers may be, for example, any business that uses technology.In return, the service provider can receive payment from the customer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still additional embodiments, the invention provides acomputer-implemented method, via a network. In this case, a computerinfrastructure, such as computer system/server 12 (FIG. 1), can beprovided and one or more systems for performing the processes of theinvention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system/server 12(as shown in FIG. 1), from a computer-readable medium; (2) adding one ormore computing devices to the computer infrastructure; and (3)incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe processes of the invention.

In addition, it should be understood that, to the extent implementationsof the platform collect, store, or employ information provided by, orobtained from, individuals (for example, trading habits) suchinformation shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information may be subject toconsent of the individual to such activity, for example, through“opt-in” or “opt-out” processes as may be appropriate for the situationand type of information. Storage and use of personal information may bein an appropriately secure manner reflective of the type of information,for example, through various encryption and anonymization techniques forparticularly sensitive information.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method implemented in a computer infrastructurehaving computer executable code tangibly embodied on a computer readablestorage medium having programming instructions operable to: determinedynamic risk assessment profiles of different users; obtain tradinginformation by ingesting streams of data received from content streamscomprising disparate electronic sources and placing this tradinginformation into an integrated data source; generate at least oneinvestment opportunity with a risk profile using the trading informationfrom the integrated data source and matching the investment opportunitywith the dynamic risk assessment profiles of a selected user or of thedifferent users; provide at least one trading recipe which is configuredto convert the at least one investment opportunity into a simplified,prepackaged executable trade for the selected user, the at least onerecipe based in the dynamic risk assessment profile of the selecteduser, and which is saved in the integrated data source; integrate thesimplified executable trade for the selected user with an externalonline trading platform of the selected user by invoking an applicationprogram interface (API) to establish an online computing connectionbetween an external online trading platform of the selected user and thesimplified, prepackaged executable trade for the selected user; executethe simplified, prepackaged executable trade through the API for theselected user on the external online trading platform; and track andmaintain a history of interactions in an event stream of the selecteduser including the simplified, prepackaged executable trade to determinebehavior of the selected user, and create reconfigured recommendationsof trading recipes for execution on the external online tradingplatform.
 2. The method of claim 1, wherein the at least one tradingrecipe is a plurality of trading recipes each of which have a differentrisk profile and execution strategies for the selected user.
 3. Themethod of claim 2, wherein the plurality of trading recipes arepreconfigured parts of the at least one investment opportunity, thatallow the selected user to execute the steps of the at least oneinvestment opportunity with a single action for any number of differenttrading scenarios.
 4. The method of claim 2, wherein the programminginstructions are operable to further determine a sentiment associatedwith the trading information and using the sentiment, in combinationwith the different risk profiles, to generate the trading recipes withthe different risk profiles and matching those to users based on theircurrent characteristics of dynamic risk preference, investment types,and current financial condition.
 5. The method of claim 4, wherein theplurality of trading recipes are provided in an opportunity packagewhich includes an opportunity execution plan as a recipe, which detailsa set of trading steps required to execute on an investment via a brokerintegration component.
 6. The method of claim 5, wherein the investmentopportunities and associated trading recipes are stored in anopportunity registry which is a dynamic collection of configuredopportunities available to the different users, and which iscontinuously updated by adding new opportunities, updatingopportunities, and removing expired opportunities including previouslyexecuted opportunity packages.
 7. The method of claim 4, wherein theprogramming instructions are operable to further match opportunitycharacteristics with users that are known to select similaropportunities from past trades and generate the trading recipes based onthe matched opportunity characteristics and user preference.
 8. Themethod of claim 1, wherein the at least one investment opportunity andassociated trading recipe is generated by a user acting as a creator andoffered to other users or to limited users based on a privileged accessand a user acting as a contributor is capable of accessing the at leastone investment opportunity from a creator of the investment opportunityand extending the investment opportunity by adding additional recipes toan opportunity package, or copy and modify the investment opportunity topresent a different investment opportunity with similar characteristics.9. The method of claim 1, wherein the at least one trading recipe isgenerated by an intelligent opportunity creator comprising a group ofintelligent agents based on at least one of past opportunities, pasttrends, news feeds, social sentiment, user comments, social sharing, andrisk profiles to generate a trading opportunity and provide the tradingopportunity to the user as the trading recipe.
 10. The method of claim9, wherein the intelligent opportunity creator continuously rates andranks the opportunities based on integration to various sentimentsobtained from different sources that are continuously scanned tocalculate a trending user sentiment on a specific opportunity and (i)targets past investors by looking at their order history and (ii) looksat an order history of current investors and recommends personalizedinvestment opportunities based on buying trends or user interactionswith current opportunities.
 11. The method of claim 9, wherein theintelligent opportunity creator is an arbiter and one or more botsworking together to generate new opportunities which are converted intoand executed by the at least one trading recipe, wherein the arbiter isa central gateway to new opportunities, and the bots list possibleopportunities to the arbiter for further processing and distribution.12. The method of claim 1, further comprising a recipe template catalogwhich provides a template where an intelligent opportunity creatorcreates instances of executable recipes from a template based on astrategy to capitalize on an identified investment opportunity based onan outlook of a creator of the identified investment opportunity or aninvestor selecting the opportunity.
 13. The method of claim 2, whereinthe plurality of trading recipes are organized into one or morecookbooks of similar type each of which are subscribed by a specificuser of interest to expedite finding of certain types of tradingrecipes.
 14. The method of claim 1, wherein the programming instructionsare operable to further provide a leaderboard which tracks a list ofsuccessful users over various periods of time based on percentage gainsachieved with the trading recipes.
 15. The method of claim 1, whereinthe trading recipes are executable by a group of users to split cost andbenefit amongst the group where the trading recipe is not executed untilrequirements of the group are met.
 16. The method of claim 1, whereinthe program instructions are further executable to: detect mobilenetwork interception and protect files comprising information; andmaintain a user repository including a profile, authentication data,preferences, account settings and history of the different users. 17.The method of claim 16, wherein the program instructions are furtherexecutable to: leverage an omni-channel approach to display andcommunicate the information to the different users such that servicescan be ingested across different interfaces; provide visualizations ofthe simplified, prepackaged executable trade for the selected user; scancyclic opportunities and patterns to create the at least one investmentopportunity; and recognize past patterns to create new opportunitieswhich are used to generate a new simplified, prepackaged executabletrade for the selected user.
 18. The method of claim 17, wherein: the atleast one trading recipe details a set of trading steps required toexecute an investment; the trading steps are packaged into thesimplified, prepackaged executable trade and include an ability for theselected user to choose between investing in an increase or in adecrease in value at varying degrees; and when the simplified,prepackaged executable trade is executed, record a type of opportunityused by the selected user for targeting the different users to be usedat a future time.
 19. The method of claim 11, wherein the arbitermaintains posting rules and excludes certain investments to keep themfrom being posted as opportunities and created into the simplified,prepackaged executable trade for the selected user.
 20. A computerprogram product comprising one or more computer readable storage mediahaving program instructions collectively stored on the one or morecomputer readable storage media, the program instructions executable to:determine dynamic risk assessment profiles of different users; obtaintrading information by ingesting streams of data received from contentstreams comprising disparate electronic sources and placing this tradinginformation into an integrated data source; generate at least oneinvestment opportunity with a risk profile using the trading informationfrom the integrated data source and matching the investment opportunitywith the dynamic risk assessment profiles of a selected user or of thedifferent users; provide at least one trading recipe which is configuredto convert the at least one investment opportunity into a simplified,prepackaged executable trade for the selected user, the at least onerecipe based in the dynamic risk assessment profile of the selecteduser, and which is saved in the integrated data source; integrate thesimplified executable trade for the selected user with an externalonline trading platform of the selected user by invoking an applicationprogram interface (API) to establish an online computing connectionbetween an external online trading platform of the selected user and thesimplified, prepackaged executable trade for the selected user; executethe simplified, prepackaged executable trade through the API for theselected user on the external online trading platform; and track andmaintain a history of interactions in an event stream of the selecteduser including the simplified, prepackaged executable trade to determinebehavior of the selected user, and create reconfigured recommendationsof trading recipes for execution on the external online tradingplatform.
 21. The computer program product of claim 20, wherein theprogram instructions are further executable to: obtain trading prospectsand sentiment of the trading prospects from a plurality of electronicsources; analyze the trading prospects and sentiment of the tradingprospects to determine a risk associated with each of the tradingprospects; package selected trading prospects as the at least oneinvestment opportunity with different fixed or configurable tradingrecipes of the at least one trading recipe, each of which have adifferent risk and/or investment outlook; provide the different fixedtrading recipes to the selected user in a personalized list; receiveexecution instructions for at least one of the different fixed tradingrecipes and accepting a simplified user action to send the executioninstructions to a brokerage account which is integrated with a platformthat generated the fixed trading recipes; and provide a confidence scorebased on raw data passing through a neural net including the sentiment,and which is used to generate the different fixed or configurabletrading recipes and optimize them over time.
 22. A system comprising: auser account configured to maintain dynamic risk assessment profiles ofdifferent users; an ingestion engine configured to obtain tradinginformation from disparate electronic sources by ingesting streams ofdata received from content streams comprising disparate electronicsources and placing this trading information into an integrated datasource; a machine learning engine configured to generate at least oneinvestment opportunity with a risk profile using the trading informationfrom the integrated data source and matching the investment opportunitywith the dynamic risk assessment profiles of a selected user or of thedifferent users; and an execution engine configured to convert the atleast one investment opportunity into a simplified, prepackagedexecutable trade for the selected user, an one or more additionalengines that: integrate the simplified executable trade for the selecteduser with an external online trading platform of the selected user byinvoking an application program interface (API) to establish an onlinecomputing connection between an external online trading platform of theselected user and the simplified, prepackaged executable trade for theselected user; execute the simplified, prepackaged executable tradethrough the API for the selected user on the external online tradingplatform; and track and maintain a history of interactions in an eventstream of the selected user including the simplified, prepackagedexecutable trade to determine behavior of the selected user, and createreconfigured recommendations of trading recipes for execution on theexternal online trading platform, wherein the ingestion engine, themachine learning engine, the one or more additional engines and theexecution engine run on a processor of the system, in combination with acomputer readable memory, one or more computer readable storage media,and program instructions collectively stored on the one or more computerreadable storage media.
 23. The system of claim 22, wherein the machinelearning engine is trained with different data to be used whengenerating the different trading recipes of different opportunities withdifferent risk profiles for the user.